%Converted with wcci2008.awk $Revision: 1.00 $ http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet
@inproceedings(Chen:2008:ijcnn,
author = "S. Chen and X. Hong and C. J. Harris",
title = "Sparse Kernel Density Estimator Using Orthogonal Regression Based on D-Optimality Experimental Design",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0003.pdf},
url = {},
size = {},
abstract = {A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen2:2008:ijcnn,
author = "S. Chen and X. Hong and C. J. Harris",
title = "Fully Complex-Valued Radial Basis Function Networks for Orthogonal Least Squares Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0004.pdf},
url = {},
size = {},
abstract = {We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen3:2008:ijcnn,
author = "S. Chen and C. J. Harris and L. Hanzo",
title = "Complex-Valued Symmetric Radial Basis Function Classifier for Quadrature Phase Shift Keying Beamforming Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0005.pdf},
url = {},
size = {},
abstract = {We propose a complex-valued symmetric radial basis function (CV-SRBF) network for nonlinear beam forming in multiple-antenna aided communication systems that employ the complex-valued quadrature phase shift keying modulation scheme. The proposed CV-SRBF classifier explicitly exploits the inherent symmetry property of the underlying data generating mechanism, and this significantly enhances the detection accuracy. An orthogonal forward selection (OFS) algorithm based on the multi-class (four-class) Fisher ratio of class separability measure (FRCSM) is derived for constructing parsimonious CV-SRBF classifiers from noisy training data. Effectiveness of the proposed approach is illustrated using simulation, and the results obtained demonstrate that the sparse CV-SRBF classifier constructed by the multi-class FRCSM-based OFS achieves excellent beamforming detection bit error rate performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang:2008:ijcnn,
author = "Yunong Zhang and Zhiguo Tan and Zhi Yang and Xuanjiao Lv and Ke Chen ",
title = "A Simplified LVI-Based Primal-Dual Neural Network for Repetitive Motion Planning of PA10 Robot Manipulator Starting from Different Initial States",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0031.pdf},
url = {},
size = {},
abstract = {This paper presents a simplified primal-dual neural network based on linear variational inequalities (LVI) for online repetitive motion planning of PA10 robot manipulator. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the repetitive-motion-planning scheme could incorporate the joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a time-varying quadratic program (QP). As a QP real-time solver, the simplified LVI-based primal-dual neural network (LVI-PDNN) is designed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. It has a simple piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic programs. The simplified LVI-PDNN model is simulated based on PA10 robot arm, and simulation results show the effective remedy of the joint angle drift problem of PA10 robot. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo:2008:ijcnn,
author = "Qinglin Guo and Ming Zhang",
title = "A Novel Approach for Fault Diagnosis of Steam Turbine Based on Neural Network and Genetic Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0033.pdf},
url = {},
size = {},
abstract = {A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacher's guidance thirdly, and the neural network is optimised by genetic algorithm lastly. Based on the ontology of neural network, the data mining algorithm for classified fault diagnosis rules about steam turbine is brought forward; its realisation process is as follows: (1) computing the measurement matrix of effect; (2) extracting rules; (3) computing the importance of rules; (4) shearing the rules by genetic algorithm. An experimental system for data mining and fault diagnosis of steam turbine based on neural network and genetic algorithm is implemented. Its diagnosis precision is 84percent. And experiments do prove that it is feasible to use the method to develop a system for fault diagnosis of steam turbine, which is valuable for further study in more depth. },
keywords = { Neural network, Genetic algorithm, Data mining, Fault diagnosis, Ontology, Steam turbine},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Neville:2008:ijcnn,
author = "R. Neville and S. Holland ",
title = "Generating Weights and Generating Vectors to Map Complex Functions with Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0034.pdf},
url = {},
size = {},
abstract = {The generation of weights is an alternative method of loading a set of weights into an artificial neural network. It is a process that transforms a trained base net by multiplying its weights by symmetric matrices [1]. These weights are then assigned to a derived net. The derived nets map symmetrically related functions. At present, the process is limited because it cannot be applied to one-to-many functions. In this paper, this limitation is overcome by generating a set of vectors from the transformed derived nets that are then used to train an ANN to map one-to-many tasks. The associated rotational symmetries performed are also specified. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Knauf:2008:ijcnn,
author = "Rainer Knauf and Yoshitaka Sakurai and Setsuo Tsuruta",
title = "Applying Knowledge Engineering Methods to Didactic Knowledge First Steps Towards an Ultimate Goal",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0038.pdf},
url = {},
size = {},
abstract = {Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. Since E-Learning systems are digital by their very nature, their introduction rises the issue of modelling the didactic design in a way that implies the chance to apply Knowledge Engineering Techniques (like Machine Learning and Data Mining). A modeling approach called storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence and successful didactic patterns can be inductively inferred by analysing the particular knowledge processing and its alleged contribution to learning success. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Igarashi:2008:ijcnn,
author = "H. Igarashi and K. Nakamura and S. Ishihara",
title = "Learning of Soccer Player Agents Using a Policy Gradient Method: Coordination Between Kicker and Receiver During Free Kicks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0040.pdf},
url = {},
size = {},
abstract = {The RoboCup Simulation League is recognised as a test bed for research on multi-agent learning. As an example of multi-agent learning in a soccer game, we dealt with a learning problem between a kicker and a receiver when a direct free kick is awarded just outside the opponent's penalty area. In such a situation, to which point should the kicker kick the ball? We propose a function that expresses heuristics to evaluate an advantageous target point for safely sending/receiving a pass and scoring. The heuristics includes an interaction term between a kicker and a receiver to intensify their coordination. To calculate the interaction term, we let kicker/receiver agents have a receiver/kicker action decision model to predict his teammate's action. The evaluation function makes it possible to handle a large space of states consisting of the positions of a kicker, a receiver, and their opponents. The target point of the free kick is selected by the kicker using Boltzmann selection with an evaluation function. Parameters in the function can be learnt by a kind of reinforcement learning called the policy gradient method. The point to which a receiver should run to receive the ball is simultaneously learnt in the same manner. The effectiveness of our solution was shown by experiments. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gorji:2008:ijcnn,
author = "Ali A. Gorji and Mohammad B. Menhaj ",
title = "Identification of Nonlinear State Space Models Using an MLP Network Trained by the EM Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0041.pdf},
url = {},
size = {},
abstract = {Identification of nonlinear state space models when no information is available from the state transition or output model has played an important role in the recent research. In this paper, we propose a new approach for modelling a discrete time nonlinear state space system with a multilayer perceptron (MLP) neural network. The expectation maximisation (EM) algorithm is used for joint parameter and state estimation of the proposed structure where the particle smoothing algorithm will be applied for estimating hidden states. Because of the non-affine structure of MLP networks compared with some other models such as radial basis functions, the gradient method is used at the M phase of the EM algorithm for parameter and noise estimation. Simulation studies show the superiority and fast convergence of our proposed structure in identification of nonlinear state space models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qiu:2008:ijcnn,
author = "Xiao-Yu Qiu and Kai Kang and Hua-Xiang Zhang",
title = "Selection of Kernel Parameters for KNN",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0049.pdf},
url = {},
size = {},
abstract = {How to choose the optimal parameter is crucial for the kernel method, because kernel parameters perform significantly on the kernel method. In this paper, a novel approach is proposed to choose the kernel parameter for the kernel nearest-neighbour classifier (KNN). The values of the kernel parameter are computed through optimising an object function designed for measuring the classification reliability of KNN. We test our approach on both artificial and real-word data sets, and the preliminary results demonstrate that our approach provides a practical solution. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Efe:2008:ijcnn,
author = "Mehmet Önder Efe and Cosku Kasnakoglu",
title = "A Comparison of Architectural Varieties in Radial Basis Function Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0050.pdf},
url = {},
size = {},
abstract = {Representation of knowledge within a neural model is an active field of research involved with the development of alternative structures, training algorithms, learning modes and applications. Radial Basis Function Neural Networks (RBFNNs) constitute an important part of the neural networks research as the operating principle is to discover and exploit similarities between an input vector and a feature vector. In this paper, we consider nine architectures comparatively in terms of learning performances. Levenberg- Marquardt (LM) technique is coded for every individual configuration and it is seen that the model with a linear part augmentation performs better in terms of the final least mean squared error level in almost all experiments. Furthermore, according to the results, this model hardly gets trapped to the local minima. Overall, this paper presents clear and concise figures of comparison among 9 architectures and this constitutes its major contribution. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li:2008:ijcnn,
author = "Minhua Li and Chunheng Wang",
title = "An Adaptive Text Detection Approach in Images and Video Frames",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0052.pdf},
url = {},
size = {},
abstract = {In this paper, an adaptive edge-based text detection approach in images and video frames is proposed. The proposed approach can adopt different edge detection methods according to the image background complexity. It mainly consists of four stages: Firstly, images are classified into different background complexities. Secondly, different edge detectors are applied on the images according to their background complexities. Thirdly, connected component analysis is adopted on the edge image to obtain text candidates. Finally, the text candidates undergo the refinement algorithm to find the exact position. Experimental results demonstrate that the proposed approach is robust to text size and could effectively detect text lines in images and video frames in both simple background and complex background. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bian:2008:ijcnn,
author = "Yong Bian and Yinqing Zhou and Chunsheng Li",
title = "Some Discrete Fourier Kind Transforms Based on FLOS",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0054.pdf},
url = {},
size = {},
abstract = {Some discrete Fourier kind transforms (DFKT) which include the discrete Fourier transform (DFT), discrete chirp Fourier transform (DCFT), three-order discrete chirp Fourier transform (TDCFT), discrete matched transform based on phase compensation (DMTPC) are studied in the symmetric alpha-stable (SaS) noise environment. Some DFKTs based on the fractional lower order statistics (FLOS) (DFKT_FLOS) are studied or presented. The DFT based on FLOS (DFT_FLOS) is studied. The DCFT based on FLOS (DCFT_FLOS), TDCFT based on FLOS (TDCFT_FLOS), DMTPC based on FLOS (DMTPC_FLOS) are presented. The simulation shows that in the impulsive noise environment, DFKT_FLOS outperforms their respective DFKT counterpart. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu:2008:ijcnn,
author = "Qingshan Liu and Jun Wang",
title = "A One-Layer Recurrent Neural Network for Convex Programming",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0064.pdf},
url = {},
size = {},
abstract = {This paper presents a one-layer recurrent neural network for solving convex programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimisation problem. Compared with the existing neural networks for optimization, the proposed neural network has lower model complexity. Moreover, the proposed neural network is proved to be globally convergent to the optimal solution(s) under some mild conditions. Simulation results show the effectiveness and performance of the proposed neural network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Horng:2008:ijcnn,
author = "Ming-Huwi Horng ",
title = "Texture Classification of the Ultrasonic Images of Rotator Cuff Diseases Based on Radial Basis Function Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0065.pdf},
url = {},
size = {},
abstract = {This article studies the usages of texture analysis methods to classify ultrasonic rotator cuff images into the different disease groups that are normal, tendon inflammation, calcific tendonitis and tendon tear. The adopted texture analysis methods include the texture feature coding method, gray-level co-occurrence matrix, fractal dimension and texture spectrum. The texture features of the four methods are used to analyse the tissue characteristic of supraspinatus tendon. The mutual information feature selection and F-scoring feature ranking method are independently used to select powerful features from the four texture analysis methods. Furthermore, the trained radial basis function network is used to classify the test images into the ones of four disease group. Experimental results tested on 85 images reveal that the classification accuracy of proposed system can achieves 84percent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ling:2008:ijcnn,
author = "S. H. Ling and H. H. C. Iu and F. H. F. Leung and K.Y. Chan",
title = "Modelling the Development of Fluid Dispensing for Electronic Packaging: Hybrid Particle Swarm Optimization Based-Wavelet Neural Network Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0066.pdf},
url = {},
size = {},
abstract = {An hybrid Particle Swarm Optimisation PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid dispensing process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO [1] is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid dispensing process on electronic packaging is employed to demonstrate the effectiveness of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheng:2008:ijcnn,
author = "Long Cheng and Zeng-Guang Hou and Min Tan and Xiuqing Wang",
title = "A Simplified Recurrent Neural Network for Solving Nonlinear Variational Inequalities",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0067.pdf},
url = {},
size = {},
abstract = {A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is reduced dramatically. In addition, the proposed neural network can also be applied to the constrained optimisation problems, and it is proved that the convex condition on the objective function of the optimization problem can be relaxed. Finally, the satisfactory performance of the proposed approach is demonstrated by simulation examples. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang:2008:ijcnn,
author = "Liying Yang and Jianda Han and Chendong Wu",
title = "A Solution of Mixed Integer Linear Programming for Obstacle-Avoided Pursuit Problem",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0070.pdf},
url = {},
size = {},
abstract = {In this paper the path planning for obstacle-avoided pursuit problem (OAP) is studied. The OAP models based on the mixed integer linear programming (MILP) is presented. In the OAP models, the dynamic equation of mass point with linear damping is taken as the state equation of vehicle's motion. Integer variables are used to describe the relative position of vehicle and obstacles. ``Expansible Target Size'' is proposed to describe the pursuit process for target step-by-step. ``Pursuit Direction'' of vehicle is defined. The Isometric Plane Method selected integer variables is used to solve MILP pursuit problem. How to select the integer variables of inner point is also given. Finally, simulations are given to show the efficiency of the method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wai:2008:ijcnn,
author = "Rong-Jong Wai and Chia-Ming Liu",
title = "Design of Dynamic Petri Recurrent-Fuzzy-Neural- Network Scheme for Mobile Robot Tracking Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0072.pdf},
url = {},
size = {},
abstract = {This study focuses on the design of a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) control for the path tracking of a nonholonomic mobile robot. In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. Moreover, the supervised gradient descent method is used to develop the online training algorithm for the DPRFNN control. In order to guarantee the convergence of path tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by numerical simulations, and its superiority is indicated in comparison with FNN, recurrent FNN (RFNN) and Petri FNN (PFNN) control systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Biazus:2008:ijcnn,
author = "Cladio J. Biazus and Mauro Roisenberg ",
title = "The Development of a Hybrid, Distributed Architecture for Multiagent Systems and its Application in Robot Soccer",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0073.pdf},
url = {},
size = {},
abstract = {Several issues still need to be unravelled in the development of multiagent systems equipped with global vision, as in robot soccer leagues. Here, we underscore three of them (1) real-time constraints on recognition of scene objects; (2) acquisition of environment knowledge; and (3) distribution and allocation of control competencies shared between the repertoire of the agent's reactive behaviour, and the central control entity's strategic and deliberative behaviour. The objective of this article is to describe the implementation of a distributed and hybrid reactive-deliberative control architecture for a multiagent system, equipped with global vision camera and agent local sensor and cameras. This multiple agent system was developed for application in robot soccer. We present the digital image processing techniques applied, as well as the proposed control architecture aimed at satisfying the constraints of this kind of application. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yin-Jie:2008:ijcnn,
author = "Lei Yin-Jie and Chen Cun-Jian and Lang Fang-Nian",
title = "Quaternion Singular Value Decomposition Approach to Color Image De-Noising",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0075.pdf},
url = {},
size = {},
abstract = {Based on quaternion, an algorithm for colour image de-noising has been proposed in this paper. According to the quaternion singular value decomposition theory, in a colour image, the singular values on the diagonal matrix, which were obtained through QSVD, represent the color images in different channels. The additive noise of a color image can be eliminated effectively by keeping the proper singular values, that represent normal image signal, and discarding the noise values. Through the color image energy model, we can reconstruct the image singular values and selectively eliminate the singular values which represent the noise. As the result, the proposed algorithm can de-noise color image rapidly, and also it can be implemented easily in practice. The experiment results prove that the algorithm is correct and the energy model is effective. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tian:2008:ijcnn,
author = "Yingjie Tian and Yunchuan Sun and Chuan-Liang Chen and Zhan Zhang",
title = "Unconstrained Transductive Support Vector Machines and Its Application",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0078.pdf},
url = {},
size = {},
abstract = {Support Vector Machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on v-Transductive Support Vector Machines for classification (v-TSVC) and construct a new algorithm - Unconstrained v-Transductive Support Vector Machines (Uv-TSVM). After researching on the special construction of primal problem in v-TSVM, we transform it to an unconstrained problem and then smooth the derived problem in order to apply usual optimisation methods. Numerical experiments prove its successful application in real life credit card dataset. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang2:2008:ijcnn,
author = "Yunong Zhang and Zenghai Chen and Ke Chen and Binghuang Cai",
title = "Zhang Neural Network without Using Time-Derivative Information for Constant and Time-Varying Matrix Inversion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0079.pdf},
url = {},
size = {},
abstract = {To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural network (ZNN) could globally exponentially converge to the exact inverse of a given time-varying matrix. To find out the effect of time-derivative term on global convergence as well as for easier hardware-implementation purposes, the ZNN model without exploiting time-derivative information is investigated in this paper for inverting online matrices. Theoretical results of both constant matrix inversion case and time-varying matrix inversion case are presented for comparative and illustrative purposes. In order to substantiate the presented theoretical results, computer-simulation results are shown, which demonstrate the importance of time derivative term of given matrices on the exact convergence of ZNN model to time-varying matrix inverses. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gorji2:2008:ijcnn,
author = "Ali A. Gorji and Mohammad B. Menhaj ",
title = "Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0080.pdf},
url = {},
size = {},
abstract = {In this paper, stochastic control of nonlinear state space models is discussed. After a brief review on nonlinear state space models, a multi layer perceptron (MLP) neural network is considered to represent the general structure of the controller. Then, an expectation maximisation (EM) algorithm joint with the particle smoothing framework are proposed for updating parameters of the MLP network. The suggested structure is also applied to the trajectory tracking of a nonlinear/nonstationary system. Simulation results show the superiority of our method in the control of nonlinear and stochastic state space models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huo:2008:ijcnn,
author = "Juan Huo and Alan Murray and Leslie Smith and Zhijun Yang",
title = "Adaptation of Barn Owl Localization System with Spike Timing Dependent Plasticity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0081.pdf},
url = {},
size = {},
abstract = {To localise a seen object, the superior colliculus of the barn owl integrates the visual and auditory localisation cues which are accessed from the sensory system of the brain. These cues are formed as visual and auditory maps, thus the alignment between visual and auditory maps is very important for accurate localisation in prey behaviour. Blindness or prism wearing may disturb this alignment. The juvenile barn owl could adapt its auditory map to this mismatch after several weeks training. Here we investigate this process by building a computational model of auditory and visual integration with map adjustment in the deep superior colliculus. The adaptation is based on activity dependent axon developing which is instructed by an inhibitory network. In the inhibitory network, the strength of the inhibition is adjusted by spike timing dependent plasticity (STDP). The simulation results are in line with the biological experiment and support the idea that the STDP is involved in the alignment of sensory maps. The system of the model provides a new mechanism capable of eliminating the disparity in visual and auditory map integration. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu2:2008:ijcnn,
author = "Feng Liu and Hu-cheng An and Jia-ming Li and Lin-dong Ge ",
title = "Blind Equalization Using v- Support Vector Regressor for Constant Modulus Signals",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0084.pdf},
url = {},
size = {},
abstract = {The support vector machine has been recently developed for blind equalisation of constant modulus signals. In this paper we propose to use a v-support vector regressor (v-SVR) for blindly equalising multipath channels because of the high generalisation ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind v-SVR equaliser. The performance of the proposed algorithm is analysed by means of computer simulations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(El-Alfy:2008:ijcnn,
author = "El-Sayed M. El-Alfy and Radwan E. Abdel-Aal",
title = "Spam Filtering with Abductive Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0085.pdf},
url = {},
size = {},
abstract = {Spam messages pose a major threat to the usability of electronic mail. Spam wastes time and money for network users and administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium to distribute harmful code and/or offensive content. In this paper, we investigate the application of abductive learning in filtering out spam messages. We study the performance for various network models on the spambase dataset. Results reveal that classification accuracies of 91.7percent can be achieved using only 10 out of the available 57 content attributes. The attributes are selected automatically by the abductive learning algorithm as the most effective feature subset, thus achieving approximately 6:1 data reduction. Comparison with other techniques such as multi-layer perceptrons and naïve Bayesian classifiers show that the abductive learning approach can provide better spam detection accuracies, e.g. false positive rates as low as 5.9percent while requiring much shorter training times. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Debnath:2008:ijcnn,
author = "Jayanta Kumar Debnath",
title = "A Modified Vector Quantization Based Image Compression Technique Using Wavelet Transform",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0087.pdf},
url = {},
size = {},
abstract = {An image compression method combining discrete wavelet transform (DWT) and vector quantisation (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate subbands (ten code books are generated using the Self Organising Feature Map algorithm, which are then used in Vector Quantisation, of the wavelet transformed subband images, i.e. one codebook for one subband). These subbands are then vector quantised. VQ indices are Huffman coded to increase the compression ratio. A novel iterative error correction scheme is proposed to continuously check the image quality after sending the Huffman coded bit stream of the error codebook indices through the channel so as to improve the peak signal to noise ratio (PSNR) of the reconstructed image. Ten error code books (each for each subband of the wavelet transformed image) are also generated for the error correction scheme using the difference between the original and the reconstructed images in the wavelet domain. The proposed method shows better image quality in terms of PSNR at the same compression ratio as compared to other DWT and VQ based image compression techniques found in the literature. The proposed method of image compression is useful for various applications in which high quality (i.e. high precision) are critical (like criminal investigation, medical imaging, etc). },
keywords = {Vector Quantisation, Wavelet Transform, Compression Ratio.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Refaat:2008:ijcnn,
author = "Khaled S. Refaat and Wael N. Helmy and AbdelRahman H. Ali and Amir F. Atiya",
title = "A New Approach for Context-Independent Handwritten Offline Diagram Recognition Using Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0089.pdf},
url = {},
size = {},
abstract = {Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio [1]. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use [2]. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10percent of the time it takes to draw one using a tool like Visio. This indicates the value of automated recognition of hand-written diagrams. Recently, applications have been developed that use online systems running on pen-input PCs that allow users to create structured diagrams by drawing the diagram on the PC tablet. The progress of offline diagram recognition is still minimal. The objective of this paper is to propose a context-independent off-line diagram recognition system. Our approach uses support vector machines [3] for recognition and Line Primitive Extraction by Interpretation of Line Continuation for segmentation [4]. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu:2008:ijcnn,
author = "Rui Xu and Steven Damelin and Donald C. Wunsch II",
title = "Clustering of Cancer Tissues Using Diffusion Maps and Fuzzy ART with Gene Expression Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0090.pdf},
url = {},
size = {},
abstract = {Early detection of a tumour's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to generate the division of cancer samples, which is useful in investigating unknown cancer types or subtypes. On the other hand, we use diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, for dimensionality reduction. The curse of dimensionality is a major problem in cancer type recognition-oriented gene expression data analysis due to the overwhelming number of measures of gene expression levels versus the small number of samples. Experimental results on the small round blue-cell tumour (SRBCT) data set, compared with other widely used clustering algorithms, demonstrate the effectiveness of our proposed method in addressing multidimensional gene expression data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Swiderski:2008:ijcnn,
author = "B. Swiderski and S. Osowski and A. Cichocki and A. Rysz",
title = "Single-Class SVM Classifier for Localization of Epileptic Focus on the Basis of EEG",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0091.pdf},
url = {},
size = {},
abstract = {The paper presents the application of a single class Support Vector Machine (SVM) for localisation of the focus region at the epileptic seizure on the basis of EEG registration. The diagnostic features used in recognition are derived from the directed transfer function description, determined for different ranges of EEG signals. The results of the performed numerical experiments for the localisation of the seizure focus in the brain have been confirmed by the real surgery of the brain for few patients. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang:2008:ijcnn,
author = "Jiao Wang and Si-wei Luo and Xian-hua Zeng ",
title = "A Random Subspace Method for Co-Training",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0092.pdf},
url = {},
size = {},
abstract = {Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabelled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The accuracy and generalizability of RASCO are analysed. The influences of the parameters of RASCO are discussed. Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ssali:2008:ijcnn,
author = "George Ssali and Tshilidzi Marwala",
title = "Computational Intelligence and Decision Trees for Missing Data Estimation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0097.pdf},
url = {},
size = {},
abstract = {This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimise an error function derived from the respective model. The models' ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13percent with the AANN based model's average accuracy increasing from 75.8percent to 86.3percent while that of the PCA-NN based model increasing from 66.1percent to 81.6percent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Honda:2008:ijcnn,
author = "Katsuhiro Honda and Hiromichi Araki and Tomohiro Matsui and Hidetomo Ichihashi",
title = "A New Approach to Robust k-Means Clustering Based on Fuzzy Principal Component Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0102.pdf},
url = {},
size = {},
abstract = {PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that is based on a fuzzy PCA-guided clustering procedure. In the proposed method, a responsibility weight of each sample in k- Means process is estimated based on the noise fuzzy clustering mechanism, and cluster membership indicators in k-Means process are derived as fuzzy principal components considering the responsibility weights in fuzzy PCA. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Aseervatham:2008:ijcnn,
author = "Sujeevan Aseervatham ",
title = "A Local Latent Semantic Analysis-Based Kernel for Document Similarities",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0104.pdf},
url = {},
size = {},
abstract = {The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorisation. In this paper, we present a semantic space constructed from latent concepts. The concepts are extracted using the Latent Semantic Analysis (LSA). To take into account of the specificity of each document category, we use the local LSA to define the global semantic space. Furthermore, we propose a weighted semantic kernel for the global space. The experimental results of the kernel, on text categorisation tasks, show that this kernel performs better than global LSA kernels and especially for small LSA dimensions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Botsch:2008:ijcnn,
author = "Michael Botsch and Josef A. Nossek",
title = "Construction of Interpretable Radial Basis Function Classifiers Based on the Random Forest Kernel",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0105.pdf},
url = {},
size = {},
abstract = {In many practical applications besides a small generalisation error also the interpretability of classification systems is of great importance. There is always a tradeoff among these two properties of classifiers. The similarity measure in the input space as defined by one of the most powerful classifiers, the Random Forest (RF) algorithm, is used in this paper as basis for the construction of Generalised Radial Basis Function (GRBF) classifiers. Hereby, interpretability and a low generalization error can be achieved. The main idea is to approximate the RF kernel by Gaussian functions in a GRBF network. This way the GRBF network can be constructed to approximate the conditional probability of each class given a query input. Since each centre in the GRBF is used for the representation of the distribution of a single target class in a localised area of the classifiers input space, interpretability can be achieved by taking account for the membership of a query input to the different localized areas. Whereas in most algorithms the pruning technique is used only to improve the generalization property, here a method is proposed how pruning can be applied to additionally improve the interpretability. Another benefit that comes along with the resulting GRBF classifier is the possibility to detect outliers and to reject decisions that have a low confidence. Experimental results underline the advantages of the classification system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(He-Yong:2008:ijcnn,
author = "Wang He-Yong ",
title = "Combination Approach of SMOTE and Biased-SVM for Imbalanced Datasets",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0107.pdf},
url = {},
size = {},
abstract = {A new approach to construct the classifiers from imbalanced datasets is proposed by combining SMOTE (Synthetic Minority Over-sampling Technique) and Biased-SVM (Biased Support Vector Machine) approaches. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. The cost of misclassifying an abnormal (interesting) example into a normal example is often much higher than that of the reverse error. It was known as a means of increasing the sensitivity of a classifier to the minority class using SMOTE over-sampling in minority class. But in this paper, it gives a good means of increasing the sensitivity of a classifier to the minority class by using SMOTE approaches within support vectors. As for support vector over-sampling, this paper proposes two different over-sampling algorithms to deal with the support vectors being over-sampled by its neighbours from the k nearest neighbors, not only within the support vectors but also within the entire minority class. Some experimental results confirms that the proposed combination approach of SMOTE and Biased-SVM can achieve better classifier performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lu:2008:ijcnn,
author = "Chi-Jie Lu and Tian-Shyug Lee and Chih-Chou Chiu",
title = "Statistical Process Monitoring Using Independent Component Analysis Based Disturbance Separation Scheme",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0109.pdf},
url = {},
size = {},
abstract = {In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, firstly, ICA is applied to manufacturing process data to find the independent components containing only the white noise of the process. The traditional control chart is then used to monitor the independent components for process monitoring. In order to evaluate the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbances are evaluated. The experimental results reveal that the proposed method outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ping:2008:ijcnn,
author = "Ling Ping and Wang Zhe and Wang Xi and Zhou Chun-guang",
title = "Derive Local Invariance Transformations from SVM",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0110.pdf},
url = {},
size = {},
abstract = {Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decision function. The corresponding IT-distance definition is consequently designed in both input space and feature space. And a classification algorithm based on IT and Nearest Neighbour rule is proposed, named as ITNN. ITNN exploits hyper sphere centres as class prototypes and labels data using a weighted voting strategy. ITNN is of computational ease brought by training dataset reduction and hyper parameter self-tuning. We describe experimental evidence of classification performance improved by ITNN on real datasets over state of the arts. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gutmann:2008:ijcnn,
author = "Michael Gutmann and Aapo Hyvärinen",
title = "Learning Encoding and Decoding Filters for Data Representation with a Spiking Neuron",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0116.pdf},
url = {},
size = {},
abstract = {Data representation methods related to ICA and sparse coding have successfully been used to model neural representation. However, they are highly abstract methods, and the neural encoding does not correspond to a detailed neuron model. This limits their power to provide deeper insight into the sensory systems on a cellular level. We propose here data representation where the encoding happens with a spiking neuron. The data representation problem is formulated as an optimisation problem: Encode the input so that it can be decoded from the spike train, and optionally, so that energy consumption is minimised. The optimisation leads to a learning rule for the encoder and decoder which features synergistic interaction: The decoder provides feedback affecting the plasticity of the encoder while the encoder provides optimal learning data for the decoder. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Morgan:2008:ijcnn,
author = "Ian Morgan and Honghai Liu and George Turnbull and David Brown",
title = "Predictive Unsupervised Organisation in Marine Engine Fault Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0122.pdf},
url = {},
size = {},
abstract = {This paper uses topological learners, the Self Organising Map in combination with the K Means algorithm to organise potential engine faults and the respective location of faults, focusing on a 12 cylinder 2 stroke marine diesel engine. This method is applied to reduce the numerosity of the data presented to a user by selecting representative samples from a number of clusters to enable efficient diagnosis. The novelty of the approach centres around the sparsity of the dataset compared to the majority of fault diagnosis techniques, and the potential for improved safety and efficiency within the marine industry compared to existing diagnosis systems. The accuracy of the SOM and K Means, as well as the Neural Gas algorithm is compared to the standard accuracy of the K Means algorithm to validate the algorithm's performance and application to this domain, where it can be seen that topological learners have much potential to be applied to the field of fault diagnosis. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen4:2008:ijcnn,
author = "Tieming Chen and Samuel H. Huang",
title = "Tree Parity Machine-Based One-Time Password Authentication Schemes",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0123.pdf},
url = {},
size = {},
abstract = {One-Time Password (OTP) is always used as the strongest authentication scheme among all password-based solutions. Currently, consumer devices such as smart card have implemented OTP based two-factor authentications for secure access controls. Such solutions are economically sound without support of time stamp mechanisms. Therefore, synchronisation of internal parameters in OTP models, such as moving factor or counter, between the client and server is the key challenge. Recently, a novel phenomenon shows that two interacting neural networks, called Tree Parity Machines (TPM), with common inputs can finally synchronise their weight vectors through finite steps of output-based mutual learning. The improved secure TPM can well be used to synchronize parameters for OTP schemes. In this paper, TPM mutual learning scheme is introduced, then two TPM-based novel OTP solutions are proposed. One is a full implementation model including initialization and rekeying, while the other is light-weight and efficient suitable for resource-constrained embedded environment. Security and performance on the proposed protocols are at final discussed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lei:2008:ijcnn,
author = "Wu Lei and Sun Feng and Cheng Jianhua ",
title = "Fault Diagnosis of FOG SINS Based on Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0125.pdf},
url = {},
size = {},
abstract = {Based on nonlinear mapping relationship between fault symptom and fault type in subsystems of FOG SINS (fiber-optic gyroscope strapdown inertial system), BP (back-propagation) and Elman neural network approaches were presented for fault diagnosis. Fault mechanism and failure behavior of FOG SINS was analyzed, then featured fault types were extracted from FOG SINS faults and the extracted features were regarded as fault symptom eigenvector. The process of fault diagnosis principal, fault diagnosis model and fault diagnosis algorithm were given using BP and Elman neural network with enough fault feature information. Trained BP and Elman were used for fault vector recognition and diagnosis to verify the proposed fault diagnosis model effectiveness and rationality. Training and test results of two neural networks were compared. The conclusion was made. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li2:2008:ijcnn,
author = "Bo Li and De-Shuang Huang and Chao Wang",
title = "Improving the Robustness of ISOMAP by De-Noising",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0127.pdf},
url = {},
size = {},
abstract = {ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classical ISOMAP except that a preprocessing strategy is introduced to remove the noises and outliers. The likelihood of each point to be a noise or an outlier is quantified by carrying out weighted principal component analysis and box statistics method is adopted to distinguish clear points from noisy ones, then ISOMAP can be performed after de-noising. Experiments on noisy s-curve and noisy Swiss-roll data validate its efficiency for improving robustness. },
keywords = { ISOMAP, robustness, de-noising},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhihui:2008:ijcnn,
author = "Huang Zhihui and Kan ShuLin and Yuan jing and Ren Yizhou and Wei Yufeng and Dong qiaoying",
title = "Intelligent Fuzzy Wavelet System for Electronic Parts Appearance Quality Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0128.pdf},
url = {},
size = {},
abstract = {With the computer accurate estimation of electronic parts defect detection in quality control play an important role in the manufacturing industry. In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system integration modeling was presented. This paper proposes a method using an adaptive system to establish the relationship between actual electronic parts defect detection and texture features of the surface image. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function. The fuzzy inference is realized by neural network and the shape of membership function can be adjusted in real time. It endues the processor with better capability of learning and self adapt. Based on establishment quality-oriented key characteristics index dynamic adaptability analysis control system, an architecture of intelligent fuzzy neural network combined integrated with quality management module of manufacturing execution system (MES) is presented. It formed a detection product appearance quality of intelligent decision support system. The accurate modeling can effectively estimate electronic parts defect detection. The results of experiments demonstrate that the system can detect product appearance quality perfectly, with a high precision and has the practicality. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jiang:2008:ijcnn,
author = "Minghui Jiang and Li Wang and Yi Shen",
title = "Asymptotic Behavior of Stochastic Cohen-Grossberg Neural Networks with Variable Delays",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0132.pdf},
url = {},
size = {},
abstract = {Using Chebyshev inequality and nonnegative semimartingale convergence theorem, the paper investigates asymptotic behaviour of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion in [9] and [10]. In the end, examples are given to verify the effective of our results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ma:2008:ijcnn,
author = "Weimu Ma and Yunong Zhang and Jiahai Wang ",
title = "MATLAB Simulink Modeling and Simulation of Zhang Neural Networks for Online Time-Varying Sylvester Equation Solving",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0133.pdf},
url = {},
size = {},
abstract = {Recently, a special kind of recurrent neural networks has been proposed by Zhang et al for online solution of Sylvester equation with time-varying coefficients. Their neural dynamics are elegantly introduced by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function, so that the computational error can vanish to zero globally and exponentially. The resultant Zhang neural networks (ZNN), perform much better on solving time-varying problems in comparison with gradient-based neural networks. MATLAB Simulink is a software package for model-based design and multi-domain simulation of dynamic systems. By using click-and-drag mouse operations, it is much easier to model and simulate complex neural systems as compared to MATLAB coding. This paper investigates the MATLAB Simulink modelling and simulative verification of ZNN models for time varying Sylvester equation solving. Computer-simulation results substantiate the ZNN efficacy on solving online the time-varying problems (specifically, the time-varying Sylvester equation). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen5:2008:ijcnn,
author = "Tao-wei Chen and Wei-dong Jin",
title = "Emitter Number Estimation from Pulse Envelope Using Information Theoretic Criterion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0134.pdf},
url = {},
size = {},
abstract = {In this paper, An approach for estimating the number of emitters from a set of interleaved pulses trains is proposed. The approach is based on the application of information theoretic criterion, which is formulated by using a new model of eigenvalues from principal component analysis (PCA) of pulse envelope vectors. In this model, the logarithm likelihood function is obtained by clustering the eigenvalues into two groups: signal and noise component group. The experimental results suggest that the present likelihood function can provide a good estimate of the dimension of signal component group from artificial data. When compared with the other information theoretic criteria, the proposed information theoretic criterion does not involve any computationally sophisticated maximum likelihood function. In addition, it is simple, intelligible, and more efficient. Computer simulations are used to show the effectiveness and feasibility of the proposed approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen6:2008:ijcnn,
author = "Tao-wei Chen and Wei-dong Jin and Jie Li",
title = "Feature Extraction Using Surrounding-Line Integral Bispectrum for Radar Emitter signal",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0135.pdf},
url = {},
size = {},
abstract = {In ever changing threat emitter environment, specific emitter identification (SEI) technology extracts subtle but persistent features from received pulse signal to create a fingerprint unique to a specific radar. Unlike conventional five parameters deinterleaving algorithm, which can be grossly ambiguous for radar emitter sorting, the SEI technology provides hardware specific identification. In this paper, we propose an approach for extracting unintentional phase modulation features caused by oscillator based on surrounding-line integral bispectrum. The quantitative features, i.e. bispectra entropy, waveform entropy and mean of surrounding-line integrated bispectra, is extracted using entropy-like function to reveal the subtle difference between emitters. Computer simulations show that how the phase-noise-induced signal changes analysis based on bispectrum approach can be used to determine which of emitters transmitted a pulse signal. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu3:2008:ijcnn,
author = "Yang Liu and Yanwei Zheng and Yuehui Chen",
title = "Ensemble Classification Based on Correlation Analysis for Face Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0136.pdf},
url = {},
size = {},
abstract = {This paper presents a new face recognition approach by using correlation analysis and ensemble classifiers based on Support Vector Machine (SVM). In this approach, image pre-processing techniques such as histogram equalisation, edge detection and geometrical transformation are first used in order to improve the quality of the face images. We further employ correlation analysis method to extract features. At last, ensemble classifiers based on SVM are selected to construct the classification committee using Binary Particle Swarm Optimisation (BPSO). Comparisons with other popular classification methods show that our scheme is very promising in face recognition. },
keywords = { Face recognition, Correlation analysis, Support vector machine, Binary particle swarm optimisation.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ouyang:2008:ijcnn,
author = "Jianjun Ouyang and Ming Xu and Yunsen Huang",
title = "A GMM Based Approach for Real-time Speech Driven 3-D Human Mouth Animation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0138.pdf},
url = {},
size = {},
abstract = {Compare with cross-modal Hidden Markov Model based and text driven human mouth synthesis schemes, this paper presents a Gaussian Mixture Model (GMM) based approach for speaker-independent real-time speech driven mouth animation. To capture the context information of continuously speaking mouth shapes in acoustic domain, the triseme based modelling technique is employed for acquiring the trisemic GMMs. To obtain the robust model parameters with the limited training data, the states tying procedure is introduced. To avoid the compatibility and ambiguity problems, the visemic questions which assigned in the leaf nodes of decision tree are generated statistically. With the modelled GMM parameters, the viterbi beam searching algorithm is applied to time align the trisemic sequence. Synthesising the recognised trisemes to the corresponding MPEG-4 FAPs represented mouth shapes, the speaking mouth can be finally animated through a smoothing process. In terms of the proposed evaluation criterion, the experimental results demonstrate that the optimising technique is promising and applicable, and also the aligning efficiency is acceptable in human vision. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mu:2008:ijcnn,
author = "Xiaoyan Mu and Paul Watta and Mohamad H. Hassoun",
title = "Analysis of a Plurality Voting-Based Combination of Classifiers",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0139.pdf},
url = {},
size = {},
abstract = {In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyse the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), M (number of classes), and P (probability that a single classifier is correct). Experimental results on the problem of human face recognition show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Youssef:2008:ijcnn,
author = "Khalid Youssef and Peng-Yung Woo",
title = "Robotic Position/Orientation Control Using Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0144.pdf},
url = {},
size = {},
abstract = {This paper studies the use of neural networks in robotic position/orientation control. The process is divided into two tasks, i.e., the inverse kinematics solution and the adaptive motor control. Simulation results of a three-link robotic arm in a two-dimensional workspace demonstrate the validity of the design. The hierarchical nature of the design allows it to be applied to more complicated systems that operate in a three dimensional workspace. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xing:2008:ijcnn,
author = "Hong-Jie Xing and Ming-Hu Ha and Da-Zeng Tian and Bao-Gang Hu",
title = "A Novel Support Vector Machine with its Features Weighted by Mutual Information",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0145.pdf},
url = {},
size = {},
abstract = {A novel support vector machine (SVM) with weighted features is proposed. To assign appropriate weights for each feature, a mutual information (MI) based approach is presented. Although the calculation of feature weights may add an extra computational cost, the proposed method generally exhibits better generalisation performance over the traditional SVM. The numerical studies on one synthetic and five existing benchmark classification problems confirm the benefits in using the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tian2:2008:ijcnn,
author = "Minghui Tian and Shouhong Wan and Yan Ji",
title = "Salient Objects Detection in Time Sequenced Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0147.pdf},
url = {},
size = {},
abstract = {Salient objects detection in time sequenced images has a very important role in many applications such as surveillance systems, tracking and recognition systems, scene analysis and so on. This paper presents a novel approach for salient objects detection in time sequenced images. The approach in this paper is based on a visual saliency model which is proposed for analysis in time sequenced images. The model in this paper is based on a bottom-up visual saliency model which is presented by Itti in 1998. Multi different features are introduced to describe salient objects globally in time sequenced images. And they are combined into a single saliency map. Salient objects in time sequenced images can be detected by the final saliency map. The detection algorithm is unsupervised and fast. The results of the experiments indicate that our approach is effective and very robust to noise, blur, contrast level and brightness level. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhao:2008:ijcnn,
author = "Huifang Zhao and Sheng Xu and Changhui Yang",
title = "A P-SVM and Chaos Based Model for High-Technology Manufacturing Labor Productivity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0148.pdf},
url = {},
size = {},
abstract = {Computing high-technology manufacturing (HTM) productivity level and growth rate have gained a renewed interest in both growth economists and trade economists. Measuring productivity performance has become an area of concern for companies and policy makers. A novel way about nonlinear regression modelling of high-technology manufacturing (HTM) productivity with the potential support vector machines (P-SVM) is presented in this paper. Optimisation of labour productivity (LP) is also presented in this paper, which is based on chaos and uses the P-SVM regression model as the objective function. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hsu:2008:ijcnn,
author = "Chun-Fei Hsu and Tsu-Tian Lee and Chih-Min Lin",
title = "Design and Simulation of Adaptive Wavelet Neuro Control with UUB Stability",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0150.pdf},
url = {},
size = {},
abstract = {This paper proposes an adaptive wavelet neuro control (AWNC) system, which is composed of a neural controller and a tangent controller. The neural controller uses a wavelet neural network to mimic an ideal controller and the tangent controller is designed to compensate for the approximation error between the ideal controller and the neural controller with using a hyperbolic tangent function. The main advantage of the proposed AWNC is that the weights are tuned on-line, and the uniformly ultimately bounded stability of the system can be guaranteed in the Lyapunov sense. Finally, to show the effectiveness of the proposed AWNC, it is applied to control a chaotic dynamic system. Simulation results verify that the proposed AWNC system can achieve favourable tracking performance. Since the developed AWNC system has no chattering phenomena in the control efforts, it is suitable for practical applications. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tay:2008:ijcnn,
author = "L. P. Tay and J. M. Zurada and L. P. Wong",
title = "HieNet Architecture Using the K-Iterations Fast Learning Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0151.pdf},
url = {},
size = {},
abstract = {This paper proposes a hierarchical architecture, HieNet, that uses the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to identify the homogeneous Feature Spaces that prepare large dimensional networks for a hierarchical organization. We illustrate how this hierarchical structure can be constructed through the recurring use of the KFLANN and support our work with experimental results.},
keywords = { Hierarchical Networks, Homogeneous Feature Spaces,Hybrid Networks, Data Presentation Sequence, Curse of Dimensionality. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Prudêncio:2008:ijcnn,
author = "Ricardo B. C. Prudêncio and Teresa B. Ludermir",
title = "Active Meta-Learning with Uncertainty Sampling and Outlier Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0153.pdf},
url = {},
size = {},
abstract = {Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive, since for each problem available for meta-example generation, it is necessary to perform an empirical evaluation of the candidate algorithms. Active Meta-Learning has been proposed to overcome this limitation by selecting only the most informative problems in the meta-example generation. In this work, we proposed an Active Meta-Learning method which combines Uncertainty Sampling and Outlier Detection techniques. Experiments were performed in a case study, yielding significant improvement in the Meta- Learning performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang:2008:ijcnn,
author = "Haibin Huang and Guangfu Ma and Yufei Zhuang ",
title = "Vehicle License Plate Location Based on Harris Corner Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0154.pdf},
url = {},
size = {},
abstract = {As the characters of license plate have enough corners, a license plate location algorithm of colour plate based on Harris corner detection is proposed. The images are first converted from RGB color model to HSI color model and filtered. Detecting corners of saturation component is then carried out according to hue component. Thirdly, the region of license plate is searched by rough and accurate location based on the characteristic of license plate. Finally, the region is binarized and the plate is corrected based on the information of corners. This algorithm can locate license plate correctly in various conditions and the cost of the system is lower than traditional methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lindgren:2008:ijcnn,
author = "Jussi T. Lindgren and Jarmo Hurri and Aapo Hyvärinen",
title = "Unsupervised Learning of Dependencies Between Local Luminance and Contrast in Natural Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0157.pdf},
url = {},
size = {},
abstract = {Separate processing of local luminance and contrast in biological visual systems has been argued to be due to the independence of these two properties in natural image data. In this paper we examine spatial, retinotopic channels formed by these two quantities and use Independent Component Analysis to study the possible dependencies between the channels. As a result, oriented, localised bandpass filter pairs are learnt, where one filter processes the luminance channel and the other the contrast channel. We study the relationship of the learnt filters and their pairings, and show that these are due to dependencies existing between local luminance and contrast. Subsequently, our results suggest that the separate processing of local luminance and contrast can not be attributed to their independence in natural images. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Soni:2008:ijcnn,
author = "Bhuman Soni and Philip Hingston ",
title = "Bots Trained to Play Like a Human are More Fun",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0158.pdf},
url = {},
size = {},
abstract = {Computational intelligence methods are well suited for use in computer controlled opponents for video games. In many other applications of these methods, the aim is to simulate near-optimal intelligent behaviour. But in video games, the aim is to provide interesting opponents for human players, not optimal ones. In this study, we trained neural network-based computer controlled opponents to play like a human in a popular first-person shooter. We then had gamers play-test these opponents as well as a hand-coded opponent, and surveyed them to find out which opponents they enjoyed more. Our results show that the neural network-based opponents were clearly preferred },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Noh:2008:ijcnn,
author = "Jin Seok Noh and Geun Hyeong Lee and Seul Jung",
title = "Position Control of a Mobile Inverted Pendulum System Using Radial Basis Function Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0160.pdf},
url = {},
size = {},
abstract = {This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function network (RBF). The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is known as a nonlinear system whose dynamics is non-holonomic. The goal is to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique (RCT) scheme is used as a neural network control method to control the MIP. The back-propagation learning algorithm for the RBF network is derived for on-line learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the position of the MIP. },
keywords = {RBF neural network, mobile inverted pendulum},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kim:2008:ijcnn,
author = "Jeong-seob Kim and Seul Jung",
title = "Implementation of the RBF Neural Chip with the On-Line Learning Back-Propagation Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0161.pdf},
url = {},
size = {},
abstract = {This article presents the hardware implementation of the Radial Basis Function (RBF) neural network whose internal weights are updated in the real-time fashion by the back-propagation algorithm. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel processing calculation of the back-propagation algorithm. The performance of the on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning program written in the MATLAB software under the same condition to check the feasibility of the implemented neural chip. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the nonlinear XOR logic. },
keywords = {RBF neural network, back-propagation algorithm, floating point processor, FPGA},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu2:2008:ijcnn,
author = "J.-X. Xu and B. Ashok and S. K. Panda and V. Bajic",
title = "Modeling Transcription Termination of Selected Gene Groups Using Support Vector Machine",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0162.pdf},
url = {},
size = {},
abstract = {In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analysing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription termination occurs. The support vector machine (SVM) approach achieves almost 90percent sensitivity, 83percent accuracy, 80percent precision and 76percent specificity on tests of the chromosomal data such as chromosome 21. The models are able to make on average just about one false prediction every 7000 nucleotides. In most cases, better results can be achieved in comparison with those reported previously on the same data sets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang2:2008:ijcnn,
author = "Xiaochun Yang and Weidong Zhao and Li Pan",
title = "Graphical Symbol Recognition in Architectural Plans with an Improved Ant-Tree Based Clustering Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0165.pdf},
url = {},
size = {},
abstract = {In this paper, an improved clustering algorithm based Ant-Tree is used for recognition of certain kind of architectural symbols with prior knowledge in engineering drawings. Symbols are segmented from an AutoCAD format drawing and a vector of invariants based on pseudo-Zernike moments is calculated to represent the graphical feature of a symbol. A normalisation method is used to make these moments invariant of translation, rotation and scaling. Then the improved Ant-Tree algorithm is applied to cluster the symbols with regard to their features. The class of target symbols can thus be got easily with the guidance of some prior knowledge. For the proposed clustering algorithm, a new initialisation method is presented with regard to the distribution of the data, and centroid approximation is also used to optimise the clustering process. Experiments show the effectiveness of our recognition approach proposed. },
keywords = {architectural symbols recognition, pseudo- Zernike moments, , Ant-Tree algorithm, feature representation},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheu:2008:ijcnn,
author = "Eng-Yeow Cheu and Hiok-Chai Quek and See-Kiong Ng",
title = "TNFIS: Tree-Based Neural Fuzzy Inference System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0166.pdf},
url = {},
size = {},
abstract = {The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the TNFIS (tree-based neural fuzzy inference system) that integrates a decision tree based classification algorithm for identification of weighted rule base. The learning algorithm is fast and highly intuitive. Simulation result of a nonlinear process modelling shows that TNFIS is able to set up reasonable membership functions and generate concise rule base to approximate a desired data set. Comparison with earlier works shows that our model performs better or comparable to other models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sheu:2008:ijcnn,
author = "Jih-Wen Sheu and Wei-Song Lin",
title = "Designing Automatic Train Regulation for MRT System by Adaptive Critic Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0167.pdf},
url = {},
size = {},
abstract = {Because of the disturbance of operation environment in Mass Rapid Transit (MRT) system, the robustness against disturbance and the schedule punctuality under control constraint are important issues to be considered in designing Automatic Train Regulation (ATR) for MRT system. In this paper, the study on suitable traffic model for designing ATR system and ATR design based on adaptive critic design (ACD) of approximated dynamic programming, specifically on dual heuristic programming (DHP) are presented. Moreover, the method to deal with control constraint and applying gain scheduling to deal with the time variant environment of MRT system are addressed as well. For comparison, simulations with real operation environment data are done for ATRs designed by both adaptive critic method and LQ optimisation method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jiang2:2008:ijcnn,
author = "Nan Jiang and Zhaozhi Zhang and Xiaomin Ma and Jian Wang and Yixian Yang",
title = "Analysis of Nonseparable Property of Multi-Valued Multi-Threshold Neuron",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0169.pdf},
url = {},
size = {},
abstract = {We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly non-separable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e., the necessary and sufficient condition for linearly nonseparable is given). Then we analyse the structures within linearly nonseparable sets: not all the vectors in a linearly nonseparable set are responsible for nonseparability. So the vectors in such set can be partitioned to separable vectors and nonseparable vectors. Finally, we discuss the learning problems for a linearly nonseparable set. Such set can be learnt by a three-layer feedforward neural network with one hidden layer. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hao:2008:ijcnn,
author = "Pei-Yi Hao and Lung-Biao Tsai and Min-Shiu Lin ",
title = "A New Support Vector Classification Algorithm with Parametric-Margin Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0173.pdf},
url = {},
size = {},
abstract = {In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v , the proposed new SV classification algorithm has advantage of using the parameter 0 ≤ ν ≤ 1 on controlling the number of support vectors. To be more precise,v is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analysed theoretically and experimentally. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo2:2008:ijcnn,
author = "Zunhua Guo and Weixin Xie and Jingxiong Huang ",
title = "Automatic Target Recognition of Aircrafts Using Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0174.pdf},
url = {},
size = {},
abstract = {The multilayered feed-forward neural network was applied to automatic target recognition using the high range resolution (HRR) profiles in this paper. To extract effective features from the HRR profiles, the product spectrum originally proposed for the speech signal processing was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay function, which could combine the information contained in the magnitude spectrum and phase spectrum of the HRR profiles and carry more details about the shape of the aircraft. A multilayered feed-forward neural network was selected as classifier. The HRR profiles were obtained using the two-dimensional back scatter distribution data of four different scaled aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum based features. The results demonstrate that the product spectrum based features outperform the original HRR profiles and the multilayered feed-forward neural network is effective for the application of automatic target recognition of aircraft.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Schemmel:2008:ijcnn,
author = "Johannes Schemmel and Johannes Fieres and Karlheinz Meier ",
title = "Wafer-Scale Integration of Analog Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0176.pdf},
url = {},
size = {},
abstract = {This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time analog neurons with up to 16k inputs. A novel interconnection and routing scheme allows the mapping of a multitude of network models derived from biology on the VLSI neural network while maintaining a high resource usage. A single 20 cm wafer contains about 60 million synapses. The implemented neurons are highly accelerated compared to biological real time. The power consumption of the dense interconnection network providing the necessary communication bandwidth is a critical aspect of the system integration. A novel asynchronous lowvoltage signaling scheme is presented that makes the wafer-scale approach feasible by limiting the total power consumption while simultaneously providing a flexible, programmable network topology. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu4:2008:ijcnn,
author = "Yang Liu and Fengqi Yu",
title = "Immunity-Based Intrusion Detection for Wireless Sensor Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0178.pdf},
url = {},
size = {},
abstract = {Wireless sensor networks (WSNs) are vulnerable to various attacks since they are distributed in unattended environments and have limited energy, storage and computation abilities. Preventive approaches can be applied to protect WSNs from some kinds of attacks. However, preventive methods are not efficient on specific attacks. So it is necessary to develop some mechanisms for intrusion detection. Intrusion detection system (IDS) not only prevents adversaries from attacking the network, but also provides attacks' features for improving the preventive algorithms. The traditional intrusion detection algorithms can't be applied directly to WSNs due to their constraints of resources. According to the problems in the current intrusion detection systems, based on immunology, we propose a novel IDS which is distributed, robust, and adaptive. The simulation results indicate that the proposed IDS has high accuracy in attack detections. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lu2:2008:ijcnn,
author = "Xin-Jiang Lu and Han-Xiong Li",
title = "Sub-Domain Intelligent Modeling Based on Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0179.pdf},
url = {},
size = {},
abstract = {In this paper, a new sub-domain intelligent modeling method based on neural networks is proposed for modeling the nonlinear multivariate process. The new modeling method decomposes the process into several levels sub-models and the low level models are the sub-model of the high level models. Since the modeling method is step by step to build the sub-models from low level models to high level models, it avoids the persistent excitation signal in multi-dimensions space, which is difficult to be produced due to the constraint of industry conditions. The accuracies and efficiencies of the modeling methodology are verified by simulation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Okun:2008:ijcnn,
author = "Oleg Okun and Giorgio Valentini",
title = "Dataset Complexity Can Help to Generate Accurate Ensembles of K-Nearest Neighbors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0180.pdf},
url = {},
size = {},
abstract = {Gene expression based cancer classification using classifier ensembles is the main focus of this work. A new ensemble method is proposed that combines predictions of a small number of k-nearest neighbour (k-NN) classifiers with majority vote. Diversity of predictions is guaranteed by assigning a separate feature subset, randomly sampled from the original set of features, to each classifier. Accuracy of k-NNs is ensured by the statistically confirmed dependence between dataset complexity, determining how difficult is a dataset for classification, and classification error. Experiments carried out on three gene expression datasets containing different types of cancer show that our ensemble method is superior to (1) a single best classifier in the ensemble, (2) the nearest shrunken centroids method originally proposed for gene expression data, and (3) the traditional ensemble construction scheme that does not take into account dataset complexity. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xiao:2008:ijcnn,
author = "Ming Xiao and Shengli Xie and Yuli Fu",
title = "Statistically Non-Sparse Decomposition of Two Underdetermined Audio Mixtures",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0181.pdf},
url = {},
size = {},
abstract = {This paper discusses the source recovery step in two-stage blind separation algorithm of underdetermined mixtures. A statistically non-sparse decomposition principle of two mixtures (2d-SNSDP), which is an extension of the SSDP algorithm about two mixtures, is proposed. It overcomes the disadvantage of the SSDP algorithm and sparse representation based on l1-norm. Compared with traditional sparse methods, it is non-sparse method, that is, almost all the recovered sources in any instant t are non-zero. Finally, several stereo audio signals experiments demonstrate its performance and practical. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(M.:2008:ijcnn,
author = "Mario I. Chacon M. and Claudia Prieto R. and R. Sandoval R. and Alejandro Rodriguez R.",
title = "A Soft Image Edge Detection Approach Based on the Time Matrix of a PCNN",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0184.pdf},
url = {},
size = {},
abstract = {Image segmentation has attracted the attention of researcher for many decades. Different approaches have been developed in order to find the solution in many different segmentation situations. In this paper we propose a novel edge detection approach aimed to generate useful information to achieve segmentation. The proposed method is based on analysis of the information provided by the time matrix generated from a pulse coupled neural network, PCNN. This information represents gray level differences among the pixel images. Two different schemes for edge detection are presented. The first scheme is developed to generate edges from coarse images and the second one to deal with more detailed edges. Similarity of this method with a previous developed method based on fuzzy edge level detection is also covered in the paper. Final results show that the proposed method may be used as a new alternative to define image edges of different levels for further analysis. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Valdes:2008:ijcnn,
author = "Julio J. Valdes and Antonio Pou and Robert Orchard",
title = "Characterization of Climatic Variations in Spain at the Regional Scale: A Computational Intelligence Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0185.pdf},
url = {},
size = {},
abstract = {Computational intelligence and other data mining techniques are used for characterising regional and time varying climatic variations in Spain in the period 1901-2005. Daily maximum temperature data from 10 climatic stations are analysed (with and without missing values) using principal components (PC), similarity-preservation feature generation, clustering, Kolmogorov-Smirnov dissimilarity analysis and genetic programming (GP). The new features were computed using hybrid optimisation (differential evolution and Fletcher- Reeves) and GP. From them, a scalar regional climatic index was obtained which identifies time landmarks and changes in the climate rhythm. The equations obtained with GP are simpler than those obtained with PC and they highlight the most important sites characterising the regional climate. Whereas the general consensus is that there has been a clear and smooth trend towards warming during the last decades, the results suggest that the picture may probably be much more complicated than what is usually assumed. },
keywords = {genetic algorithms, genetic programming},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu:2008:ijcnn,
author = "Ganggang Yu and Fengqi Yu and Lei Feng",
title = "A Three Dimensional Localisation Algorithm Using a Mobile Anchor Node under Wireless Channel",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0186.pdf},
url = {},
size = {},
abstract = {Localisation is one of the crucial issues in wireless sensor networks. In range-based mechanisms, the nodes obtain pairwise distances or angles with extra hardware for high localisation accuracy. On the other hand, the range-free schemes obtain lower localisation accuracy at low hardware cost. To improve location accuracy, we present a three dimensional range-free localisation scheme by using a mobile anchor node equipped with a GPS. The mobile anchor node carried in an aero plane flies over the sensor node area and broadcasts its current position periodically. A sensor node in the area computes its own location using the position of the mobile anchor node where the maximum RSSI is received by the sensor node. In our scheme, neither extra hardware on each sensor node nor communications between sensor nodes is needed. Our proposed scheme is simulated in Opnet and simulation results show that our scheme performs better than other range-free localisation algorithms using mobile beacon nodes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Takahashi:2008:ijcnn,
author = "Norikazu Takahashi and Yasuhiro Minetoma ",
title = "On Asymptotic Behavior of State Trajectories of Piecewise-Linear Recurrent Neural Networks Generating Periodic Sequence of Binary Vectors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0189.pdf},
url = {},
size = {},
abstract = {Recently a sufficient condition for the recurrent neural network with the piecewise-linear output characteristic to generate a prescribed periodic sequence of binary vectors such that every two consecutive vectors differ in exactly one component has been derived. If a recurrent neural network satisfies this condition, it is guaranteed that any state trajectory of the network passes through the periodic sequence of regions corresponding to the periodic sequence of binary vectors. However, the asymptotic behaviour of the state trajectories has not been clarified yet. In this paper, we study asymptotic behaviour of state trajectories of recurrent neural networks satisfying the above-mentioned sufficient condition, and derive a criterion for state trajectories to converge a unique limit cycle. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Feng:2008:ijcnn,
author = "Lei Feng and Fengqi Yu",
title = "A Contention-Based MAC for Wireless Sensor Networks Including a Mobile Node",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0191.pdf},
url = {},
size = {},
abstract = {Wireless sensor networks with many inexpensive sensor nodes allow users to accurately monitor a remote environment. Usually one or several mobile nodes are used to collect and combine sensor data from each individual stationary node. These networks require robust wireless communication protocols that are energy efficient and provide low latency. Motivated by these applications, we develop a novel medium access control (MAC) protocol for this kind of applications. The sensor nodes are expected to remain inactive for most of time, but become active when a mobile node is nearby. A few techniques for energy saving are developed in our protocol, e.g., true-sleep and pseudo-sleep modes are introduced to reduce energy consumption. They are implemented by radio-triggered circuit which wakes up the stationary nodes as a mobile node moves into the stationary nodes' reception range. The proposed protocol is simulated in OPNET Modeller 10.5. The simulation results show that the proposed protocol has better performance than S-MAC and EAR (Eavesdrop-And-Register) in terms of latency, energy efficiency, and throughput. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kurosawa:2008:ijcnn,
author = "Yoshiaki Kurosawa ",
title = "Incremental Learning for Feature Extraction Filter Mask Used in Similar Pattern Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0194.pdf},
url = {},
size = {},
abstract = {The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and Neural Networks (NN) are explained and the specifications of this method are described as an NN application.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu2:2008:ijcnn,
author = "Zhiwen Yu and Xing Wang and Hau-San Wong",
title = "Ensemble Based 3D Human Motion Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0196.pdf},
url = {},
size = {},
abstract = {Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an Ensemble based Human Motion Classification Approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular value decomposition (SVD) is adopted to reduce the dimensionality of all the feature vectors. In the following step, a cluster ensemble approach is designed to construct the consensus matrix from the feature vectors. Finally, the normalised cut algorithm is applied to partition the consensus matrix and assign the feature vectors into the corresponding clusters. Experiments on the CMU database illustrate that the proposed approach achieves good performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yuan:2008:ijcnn,
author = "Jin Yuan and Kesheng Wang and Tao Yu and Xuemei Liu",
title = "Incorporating Fuzzy Prior Knowledge into Relevance Vector Machine Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0198.pdf},
url = {},
size = {},
abstract = {Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper presents a simple but effective re-sampling algorithm based on piecewise differential interpolation and it is integrated with the sparse Bayesian learning framework for fuzzy model fused Relevance Vector Machine (RVM) regression. By using re-sampling algorithm encoded derivative regularisation, the prior knowledge is translated into a pseudo training data-set, which finally is trained by the adaptive Gaussian kernel RVM to obtain more sparse solution. A preliminary empirical study shows that combining prior knowledge with training examples can dramatically improve the regression performance, particularly when the training data-set is limited. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Olier:2008:ijcnn,
author = "Ivan Olier and Alfredo Vellido",
title = "A Variational Formulation for GTM Through Time",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0201.pdf},
url = {},
size = {},
abstract = {Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualisation of multivariate, real valued, i.i.d. data. It was also extended to deal with noni. i.d. data such as multivariate time series in a variant called GTM Through Time (GTM-TT), defined as a constrained Hidden Markov Model (HMM). In this paper, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework and provide an illustrative example of its application. This approach handles the presence of noise in the time series, helping to avert the problem of data overfitting. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Vellido:2008:ijcnn,
author = "Alfredo Vellido and Jorge Velazco",
title = "The Effect of Noise and Sample Size on an Unsupervised Feature Selection Method for Manifold Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0202.pdf},
url = {},
size = {},
abstract = {The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to Generative Topographic Mapping (GTM), a manifold learning constrained mixture model that provides data visualisation. Some of the results of a previous partial assessment of this unsupervised feature selection method for GTM suggested that its performance may be affected by insufficient sample size and by noisy data. In this brief study, we test in some detail such limitations of the method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cao:2008:ijcnn,
author = "Yi Cao and Yaochu Jin and Michal Kowalczykiewicz and Bernhard Sendhoff",
title = "Prediction of Convergence Dynamics of Design Performance Using Differential Recurrent Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0203.pdf},
url = {},
size = {},
abstract = {Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimisation problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have been seen using feedforward neural networks to approximate CFD models. However, feedforward neural network models cannot capture well the dynamics of the differential equations. Thus, training data from a large number of different designs are needed to train feedforward neural network models to achieve reliable generalisation. In this work, a technique using differential recurrent neural networks has been proposed to predict the performance of candidate designs before the CFD simulation is fully converged. Compared to existing methods based on feedforward neural networks, this approach does not need a large number of previous designs. Case studies show that the proposed method is very promising. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li3:2008:ijcnn,
author = "Bo Li and De-Shuang Huang and Kun-Hong Liu",
title = "Constrained Maximum Variance Mapping",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0210.pdf},
url = {},
size = {},
abstract = {In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterised, the proposed method focuses on developing a linear transformation that can maximise the distances matrix between all the manifolds under the constraint of locality preserving. Then, YALE face database, ORL face database are all taken to examine the effectiveness and efficiency of the proposed method. Experimental results validate that the proposed approach is superior to other widely used feature extraction methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu3:2008:ijcnn,
author = "Gang Xu and Jie Gao",
title = "A New Method of Weak Signal Detection Based on Improved Matching Pursuit Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0212.pdf},
url = {},
size = {},
abstract = {In this paper, the theory of sparse decomposition is introduced to weak signal detection, and the improved matching pursuit (MP) algorithm is studied to accomplish anti-interference process of some typical signals, such as a weak sine wave signal submerged in strong noises. The improved matching pursuit algorithm uses dual-parameter Gabor dictionary, and the iterative times can be modified in accordance with the signal to noise ratio (SNR), the genetic algorithm is also used to improve the efficiency of searching time-frequency atoms, thereby achieving high searching efficiency of time-frequency atoms and rapid noise restraining. The results of experiments indicated that the improved algorithm can effectively increase the searching speed by approximately 100 times and reduce the noises above SNR-15. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu4:2008:ijcnn,
author = "Gang Xu and Xu Meng",
title = "Detection and Recognition on Parameters of Object's Internal Structure",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0213.pdf},
url = {},
size = {},
abstract = {The detection of objects, the key technology in the field of image recognition, is the base of accuracy improvement of image recognition process. In this paper, a model is provided for detection and recognition of a object's internal structure. This model, which is based on Moment and Hough, combines geometric features as its parameter identification, and its evaluation criteria is matching percent and algorithm efficiency. The recognition, position and detection of graphics' internal structure can be completed effectively and accurately. In addition, good experimental results were obtained by using this algorithm, even though the objects were covered by each other or nested, which proves the model is applicable on practical application. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Isaacs:2008:ijcnn,
author = "Amitay Isaacs and Vishwas Puttige and Tapabrata Ray and Warren Smith and Sreenatha Anavatti",
title = "Development of a Memetic Algorithm for Dynamic Multi-Objective Optimization and Its Applications for Online Neural Network Modeling of UAVs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0215.pdf},
url = {},
size = {},
abstract = {Dynamic Multi-objective Optimisation (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on test problems and a handful of such applications have been reported. In this paper, we introduce a Memetic Algorithm (MA) and illustrate its performance for online Neural Network (NN) identification of the Multi-Input Multi-Output Unmanned Aerial Vehicle (UAV) system. As a typical case, the longitudinal model of the UAV is considered and the performance of a NN trained with the memetic algorithm is compared to another trained with Levenberg-Marquardt training algorithm using mini-batches. The memetic algorithm employs an orthogonal epsilon-constrained formulation to deal with multiple objectives and a Sequential Quadratic Programming (SQP) solver is embedded as its local search mechanism to improve the rate of convergence. The performance of the memetic algorithm is presented for two benchmarks Fisher's Discriminant Analysis (FDA), FDA1 and modified FDA2 before highlighting its benefits for online NN model identification for UAVs. Observations from our recent work [1] indicated that Mean Square Error (MSE) alone may not always be a good measure for training the networks. Hence the MSE and maximum absolute value of the instantaneous error is considered as objectives to be minimised which requires a Dynamic MO algorithm. The proposed memetic algorithm is aimed to solve such identification problems and the same can be extended to control problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Teh:2008:ijcnn,
author = "Chee Siong Teh and Md. Sarwar ZahanTapan",
title = "A Hybrid Supervised ANN for Classification and Data Visualization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0217.pdf},
url = {},
size = {},
abstract = {Supervised ANNs such as Learning Vector Quantisation (LVQs) and Multi-Layer Perceptrons (MLPs) usually do not support data visualisation beside classification. Unsupervised visualisation focused ANNs such as Self-organising Maps (SOM) and its variants such as Visualization induced SOM (ViSOM) on the other hand, usually do not optimise data classification as compared with supervised ANNs such as LVQ. Thus to provide supervised classification and data visualisation simultaneously, this work is motivated to propose a novel hybrid supervised ANN of LVQ with AC by hybridising LVQ and modified Adaptive Coordinate (AC) approach. Empirical studies on benchmark data sets proven that, LVQwithAC was able to provide superior classification accuracy than SOM and ViSOM. Beside LVQwithAC was able to provide data topology, data structure, and inter-neuron distance preserve visualisation. LVQwithAC was also proven able to perform promising classification among other supervised classifiers besides its additional data visualisation ability over them. Thus, for applications requiring data visualization and classification LVQwithAC demonstrated its potential if supervised learning is all possible. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wu:2008:ijcnn,
author = "Ketong Wu and Fan Cen and Huizhi Cai",
title = "SVR-Based Approach to Improve Active Sonar Detection in Reverberation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0219.pdf},
url = {},
size = {},
abstract = {Whitening method is widely used for improving active sonar detection in reverberation environment, which is equivalent to AR model estimation. However, traditional whitening methods suffer from several problems due to the varying statistics and nonlinearity of reverberation noise. In this paper, we use Support Vector Regression (SVR) to obtain the parameters of a whitening filter. The algorithm of SMO without bias is used to train SVR and three speed-up approaches are proposed. The SVR parameters C and p are selected by evaluating the detection performance. The ability of SVR prewhitener is verified on real lake data. Experimental results show that SVR prewhitener outperforms traditional methods significantly and provides an excellent performance even under low signal-to-reverberation ratio (SRR) and low-Doppler conditions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu3:2008:ijcnn,
author = "Zhiwen Yu and Zhongkai Deng and Hau-San Wong and Xing Wang ",
title = "Fuzzy Cluster Ensemble and its Application on 3D Head Model Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0220.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new algorithm called fuzzy cluster ensemble algorithm (FCEA) which integrates the fuzzy logic theory and traditional cluster ensembles for 3D head model classification. Specifically, FCEA consists of two parts: (i) data processing on the distributed locations and (ii) data fusion on the centralised location. In the distributed locations, data processing includes (i) extracting feature vectors from 3D head models, (ii) performing basic fuzzy clustering algorithm to obtain fuzzy membership matrix, while data fusion on the centralized location contains (i) creating a fuzzy cluster ensemble constructor by integrating different fuzzy membership matrices from the distributed locations, and (ii) obtaining the final results of 3D head model classification based on the fuzzy logic theory and the fuzzy cluster ensemble constructor. The experiments show that FCEA works well on 3D head model database. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sun:2008:ijcnn,
author = "Zhanquan Sun and Yinglong Wang and Jingshan Pan",
title = "Short-Term Traffic Flow Forecasting Based on Clustering and Feature Selection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0221.pdf},
url = {},
size = {},
abstract = {Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on different time sections respectively. Traffic flow forecasting usually is real-time and too many forecasting variables will reduce the real-time performance. So the selection of the most informative forecasting variable combination is significant. It can save computation cost and improve forecasting precision. In this paper, information bottleneck theory based on extended entropy is used to partition traffic flow of a day into different time sections. Corresponding to each time section, feature selection based on mutual information is generalised to regression problems and is used to select the most informative variable combination. Selected variables are input to Support Vector Machines (SVM) for traffic flow forecasting. Bayesian inference is used to determine the kernel parameters of SVM. The efficiency of the method is illustrated through analysing the traffic data of Jinan urban transportation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu4:2008:ijcnn,
author = "Zhiwen Yu and Xing Wang and Hau-San Wong and Zhongkai Deng ",
title = "Pattern Mining Based on Local Distribution",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0222.pdf},
url = {},
size = {},
abstract = {Pattern mining gains more and more attention due to its useful applications in many areas, such as machine learning, database, multimedia, biology, and so on. Though there exist a lot of approaches for pattern mining, few of them consider the local distribution of the data. In the paper, we not only design six challenge datasets related to the local patterns, but also propose a new pattern mining algorithm based on local distribution. Unlike traditional pattern mining algorithms, our new algorithm first creates a local distribution for each data point by a random approach. Then, the distribution curve of each data point is simulated by the sum of low frequency curves obtained by the wavelet approach. In the third step, the coefficients of these low frequency curves for each data point are clustered by the normalised cut approach. Finally, the patterns of the datasets are obtained by the new pattern mining algorithm. The experiments show that our new algorithm outperforms traditional unsupervised learning approaches, such as K-means, EM, spectral clustering algorithm (SCA), and so on, on these six new datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhongkai:2008:ijcnn,
author = "Zhiwen Yu Zhongkai and Deng Hau-SanWong",
title = "Knowledge based Cluster Ensemble",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0223.pdf},
url = {},
size = {},
abstract = {Although there exist a lot of cluster ensemble approaches, few of them consider the prior knowledge of the datasets. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the dataset into the cluster ensemble framework. Specifically, the prior knowledge of the dataset is first represented by the side information which is encoded as pairwise constraints. Then, KCE generates a set of cluster solutions by the basic clustering algorithm. Next, KCE transforms the pairwise constraints to the confidence factor of the cluster solutions. In the following, the new data matrix is constructed by considering all the cluster solutions and their corresponding confidence factor. Finally, the results are obtained by partitioning the consensus matrix. The experiments illustrate that (1) KCE works well on the real datasets; (2) KCE outperforms most of the state-of-art cluster ensemble approaches. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Che:2008:ijcnn,
author = "Xi-Long Che and Liang Hu",
title = "Parallel Multidimensional Step Search Algorithm for Epsilon-Insensitive Support Vector Regression in Time Series Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0225.pdf},
url = {},
size = {},
abstract = {Recently, Epsilon-Insensitive Support Vector Regression (ε-SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search (PMSS) method, standard ε-SVR method is extended to a systematic approach for user to finish model selection with high prediction accuracy. Experiments with both simulation data set and practical data set were performed on computing nodes in Grid environment. Experimental results were analysed with statistical method to validate the effectiveness and accuracy of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Dozono:2008:ijcnn,
author = "Hiroshi Dozono and Masanori Nakakuni",
title = "An Integration Method of Multi-Modal Biometrics Using Supervised Pareto Learning Self Organizing Maps",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0226.pdf},
url = {},
size = {},
abstract = {This paper proposes a method for the integration of multi-modal biometrics. As the conventional authentication method, password system is mostly used. But, password mechanism has many issues. In order to solve the problems, biometric authentication methods are often used. But, the authentication method using biological characteristics, such as fingerprint, also has some problems. In this paper, we propose a authentication method using multi-modal behaviour biometrics sampled from keystroke timings and handwritten patterns. And Supervised Pareto learning Self Organising maps which integrate the multimodal vectors is proposed. The performance of this method is confirmed by the authentication experiments. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qinghua:2008:ijcnn,
author = "Wang Qinghua and Zhang Youyun and Zhu Yongshen and Yang Junyan",
title = "Fault Diagnosis of Time-Frequency Images Based on Non-Negative Factorization and Neural Network Ensemble",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0227.pdf},
url = {},
size = {},
abstract = {Considering unstable characteristics of vibration signals with mechanical failure, the Wigner-Ville distributions (WVD) of vibration acceleration signals, which were acquired from the cylinder head in eight different states of valve train, were calculated and displayed in grey images. Non-negative matrix factorisation (NMF) as a useful decomposition for multivariate data and neural network ensembles (NNE) with better generalisation capability for classification than a single NN were introduced to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time-frequency distributions. The experimental results show that the time-frequency images can be classified accurately by the proposed methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Taguchi:2008:ijcnn,
author = "Y-h. Taguchi and M. Michael Gromiha",
title = "Gene Ontology Term Prediction Based Upon Acid Occurence",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0229.pdf},
url = {},
size = {},
abstract = {Usually prediction of molecular functions of proteins from their amino acid sequences is based upon sequence similarity with proteins of known functions. However, it is well known that function is mainly dependent upon protein structures than sequences. Since structures are often independent of sequence, it is important to predict function without sequence similarities. Here we propose a method based upon amino acid occurrence for predicting Gene Ontology (GO) term. We have tested the method in a set of 3212 proteins in Protein Data Bank with less than 40percent sequence identity. Our method achieved more than 50percent sensitivity and 20percent precision for c.a. 20 selected GO terms among the most frequent 557 GO terms. Mean sensitivity, Specificity, precision, and accuracy for relatively rare (but majority) 402 GO terms among 557 GO terms are 13percent, 99percent, 9percent and 99percent, respectively. They are significantly larger than expected values of less than 2percent under assuming random selection. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li4:2008:ijcnn,
author = "Boyang Li and Jinglu Hu and Kotaro Hirasawa",
title = "Financial Time Series Prediction Using a Support Vector Regression Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0231.pdf},
url = {},
size = {},
abstract = {This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller dimension. Then the transformed outputs from the transformation layer are used as the inputs for the SVR in prediction layer. The whole SVR network gives an online prediction of financial time series. Simulation results on the prediction of currency exchange rate between US dollar and Japanese Yen show the feasibility and the effectiveness of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Thakur:2008:ijcnn,
author = "R. S. Thakur and R. C. Jain and K. R. Pardasani",
title = "Graph Theoretic Based Algorithm for Mining Frequent Patterns",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0234.pdf},
url = {},
size = {},
abstract = {The primary goals of any frequent pattern mining algorithm are to reduce the number of candidates generated and tested as well as number of scan of database required and scan the database as small as possible. In this paper, we focus on reducing database scans and avoiding candidate generation. To achieve this objective a graph theoretic algorithm has been developed. The whole database is compressed by converting into pattern base in the form of a directed graph which is stored in the form of an Adjacency Matrix. This Adjacency Matrix is very small as compared to the size of database. This frequent pattern mining is done by performing operation on adjacency matrix of directed graph. The prominent feature of this method is it requires only single scan of the database for finding frequent patterns. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhu:2008:ijcnn,
author = "Ming Zhu and Weidong Jin and Laizhao Hu",
title = "Radar Emitter Signal Recognition Based on Atomic Decomposition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0236.pdf},
url = {},
size = {},
abstract = {In this paper, a novel approach based on Gaussian Chirplet Atoms is presented to automatically recognise radar emitter signals. Firstly, based on the over-completed dictionary of Gaussian Chirplet atoms, the improved matching pursuit (MP) algorithm is applied to extract the features of the time-frequency atoms from the typical radar emitter signals, and FFT is introduced to effectively reduce the time complexity of searching step of MP. Secondly, reduce dimension of the feature parameters to re-extract the classification feature vectors. Finally, adopt the hierarchy decision strategy to realise automatic classification. The simulation experiment result shows that the classification feature vector has good properties of clustering the same and separating the different kind of radar emitter signals. Over 90percent recognition accuracy can be achieved as the signal-to-noise ratio is greater than -4dB. Therefore, the approach of signal recognition is feasible in the practical engineering area. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jiang3:2008:ijcnn,
author = "Zhu Jiang and Yan Zhang and Yong-xuan Huang and Ji-sheng Li",
title = "Calibration of Traffic Dynamics Models with Data Mining",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0237.pdf},
url = {},
size = {},
abstract = {Speed-density relationships are one of models used by a mesoscopic traffic simulator to represent traffic dynamics. While the classical speed-density relationships provide a useful insight into the traffic dynamics problem and have theoretical value to traffic flow, for such applications they are limited. This paper focuses on calibrating parameters for the speed-density relationships by using data mining methods such as locally weighted regression, k-means, k-nearest neighbourhood classification and agglomerative hierarchical clustering. Meanwhile, in order to improve the precision of the parametric calibration, we also use densities and flows as variables to calibrate parameters. The proposed approach is tested with sensor data from the 3rd ring road in Beijing. The test results show that the proposed algorithm has great performance on the parametric calibration of the speed-density relationships. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Siwek:2008:ijcnn,
author = "K. Siwek and S. Osowski and K. Garanty and M. Sowiński",
title = "Ensemble of Neural Predictors for Forecasting the Atmospheric Pollution",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0239.pdf},
url = {},
size = {},
abstract = {The paper presents the application of an ensemble of neural predictors for forecasting the daily meteorological PM10 pollution. The Support Vector Machine has been used as the basic predicting network. The bagging technique has been applied to adapt different predictors. The results of many predictors have been combined together to form final forecasting. The blind source separation has been applied as the integration tool. The results of forecasting of the real pollution measured in the northern region of Poland have been presented and discussed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lemaire:2008:ijcnn,
author = "Vincent Lemaire and Raphael Feraud and Nicolas Voisine ",
title = "Contact Personalization Using a Score Understanding Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0240.pdf},
url = {},
size = {},
abstract = {This paper presents a method to interpret the output of a classification (or regression) model. The interpretation is based on two concepts: the variable importance and the value importance of the variable. Unlike most of the state of art interpretation methods, our approach allows the interpretation of the model output for every instance. Understanding the score given by a model for one instance can for example lead to an immediate decision in a Customer Relational Management (CRM) system. Moreover the proposed method does not depend on a particular model and is therefore usable for any model or software used to produce the scores. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tang:2008:ijcnn,
author = "Yaohua Tang and Jinghuai Gao and Guangzhao Cui",
title = "Ensemble Learning with Generalization Performance Measurement and Negative Correlation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0243.pdf},
url = {},
size = {},
abstract = {Conventional ensemble learning algorithms based on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimisation principle. When SVM is used as the component learner, the generalisation ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation learning theory, a new selective SVM ensemble learning method is proposed. Experiments on real world data sets from UCI were carried out to demonstrate the effectiveness of this method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fu:2008:ijcnn,
author = "Yu Fu and Antony Browne",
title = "Investigating the Influence of Feature Correlations on Automatic Relevance Determination",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0244.pdf},
url = {},
size = {},
abstract = {Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper, we demonstrate that the predicted local relevance rank for the relevant features could be influenced by their highly correlated redundant features, according to the strength of their correlations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Luo:2008:ijcnn,
author = "Zhihui Luo and David Bell and Barry McCollum and Qingxiang Wu",
title = "Learning to Select Relevant Perspective in a Dynamic Environment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0246.pdf},
url = {},
size = {},
abstract = {When an agent observes its environment, there are two important characteristics of the perceived information. One is the relevance of information and the other is redundancy. The irrelevant and redundant features which commonly exists within an environment, commonly leads to agent state explosion and associated high computational cost within the learning process. This paper presents an efficient method concerning both the relevance of information and the correlation in order to improve the learning of reinforcement learning agent. We introduce a new concurrent online learning method to calculate the match count C(s) and relevance degree I(s) to quantify the redundancy and correlation of features with respect to a desired learning task. Our analysis shows that the correlation relationship of the features can be extracted and projected to concurrent biased learning threads. By comparing the commonalities of these learning threads, we can evaluate the relevance degree of a feature that contributes to a particular learning task. We explain the method using random walk examples and then demonstrate the method on the chase object domain. Our validation results show that, using the concurrent learning method, we can efficiently detect redundancy and irrelevant features from the environment on sequential tasks, and significantly improve the efficiency of learning. After relevant features are extracted, the agent can remarkably accelerate its succeeding learning speed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bondu:2008:ijcnn,
author = "Alexis Bondu and Vincent Lemaire",
title = "Adaptive Curiosity for Emotions Detection in Speech",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0247.pdf},
url = {},
size = {},
abstract = {Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions in human machine interactions for intelligent system design. The main difficulty to implement automatic detection of emotions in speech is the prohibitive labelling cost of data. Active learning tries to select the most informative examples to build a training set for a predictive model. In this article, the adaptive curiosity framework is used in terms of active learning terminology, and directly compared with existing algorithms on an emotion detection problem. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Meftah:2008:ijcnn,
author = "B. Meftah and A. Benyettou and O.Lezoray and W. QingXiang",
title = "Image Clustering with Spiking Neuron Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0248.pdf},
url = {},
size = {},
abstract = {The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Meuth:2008:ijcnn,
author = "Ryan J. Meuth and Paul Robinette and Donald C. Wunsch II",
title = "Computational Intelligence Meets the NetFlix Prize",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0253.pdf},
url = {},
size = {},
abstract = {The NetFlix Prize is a research contest that will award 1 Million to the first group to improve NetFlix's movie recommendation system by 10percent. Contestants are given a dataset containing the movie rating histories of customers for movies. From this data, a processing scheme must be developed that can predict how a customer will rate a given movie on a scale of 1 to 5. An architecture is presented that uses the Fuzzy-Adaptive Resonance Theory clustering method to create an interesting set of data attributes that are input to a neural network for mapping to a classification. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ni:2008:ijcnn,
author = "Yizhao Ni and Carlton Chu and Craig J Saunders and John Ashburner",
title = "Kernel Methods for fMRI Pattern Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0254.pdf},
url = {},
size = {},
abstract = {In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one uses the Cosine Transform to remove low-frequency drifts over time and the other involves using prior knowledge about the spatial contribution of different brain regions for the various tasks. Our experiment results on the PBAIC2007 competition data set show a great improvement for brain activity prediction, especially on some sensory experience such as hearing and vision. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang2:2008:ijcnn,
author = "Dianhui Wang ",
title = "Modeling Performance Enhancement with Constrained Linear Filters",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0255.pdf},
url = {},
size = {},
abstract = {Estimation of plant Jacobian is necessary for successful control of nonlinear systems using neural networks with the specialised learning scheme. Our previous study showed that neuro-emulators provide a better estimation of the plant Jacobian using a new cost function for learning during the course of dynamic modelling and control. This paper presents an approach for further enhancing the estimation of the plant Jacobian, where a constrained linear filter is proposed to improve the quality of Jacobian teacher signals for on-line modelling. Simulations, including both modeling and adaptive control of a unknown nonlinear system, were carried out to demonstrate the usefulness of the proposed strategy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tivive:2008:ijcnn,
author = "Fok Hing Chi Tivive and Abdesselam Bouzerdoum",
title = "A Biologically Inspired Visual Pedestrian Detection System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0257.pdf},
url = {},
size = {},
abstract = {In this paper, we present a biologically inspired method for detecting pedestrians in images. The method is based on a convolutional neural network architecture, which combines feature extraction and classification. The proposed network architecture is much simpler and easier to train than earlier versions. It differs from its predecessors in that the first processing layer consists of a set of pre-defined nonlinear derivative filters for computing gradient information. The subsequent processing layer has trainable shunting inhibitory feature detectors, which are used as inputs to a pattern classifier. The proposed pedestrian detection system is evaluated on the DaimlerChrysler pedestrian classification benchmark database and its performance is compared to the performance of support vector machines and Adaboost classifiers. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheung:2008:ijcnn,
author = "Chi-Chung Cheung and Sin-Chun Ng",
title = "Backpropagation with Two-Phase Magnified Gradient Function",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0258.pdf},
url = {},
size = {},
abstract = {Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications have been proposed to improve the performance of BP, and BP with Magnified Gradient Function (MGFPROP) is one of the fast learning algorithms which improve both the convergence rate and the global convergence capability of BP [19]. MGFPROP outperforms many benchmarking fast learning algorithms in different adaptive problems [19]. However, the performance of MGFPROP is limited due to the error overshooting problem. This paper presents a new approach called BP with Two-Phase Magnified Gradient Function (2P-MGFPROP) to overcome the error overshooting problem and hence speed up the convergence rate of MGFPROP. 2P-MGFPROP is modified from MGFPROP. It divides the learning process into two phases and adjusts the parameter setting of MGFPROP based on the nature of the phase of the learning process. Through simulation results in two different adaptive problems, 2P-MGFPROP outperforms MGFPROP with optimal parameter setting in terms of the convergence rate, and the improvement can be up to 50percent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Rui:2008:ijcnn,
author = "Lin Rui and Du Zhijiang and He Fujun and Kong Minxiu and Sun Lining",
title = "Tracking a Moving Object with Mobile Robot Based on Vision",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0260.pdf},
url = {},
size = {},
abstract = {The paper proposes a real-time tracking algorithm for a moving object with mobile robot based on vision using adaptive colour matching and Kalman filter. The adaptive colour matching can limit the region containing moving object on vision image plane. It can adjust colour matching threshold to reduce the influence of lighting variations in the scene. Kalman filter is used as our prediction module to calculate motion vectors of moving object in the robot coordinate system. A view window containing the position of moving object estimated by Kalman filter is determined on image plane to reduce the image processing area. Colour matching threshold can adjust itself adaptively in view window, which is used as an updating module. Experimental results show that the algorithm can adapt to lighting variations and has good tracking precision. It can also be implemented in real time. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Han:2008:ijcnn,
author = "Xue Han and Ma Hong-xu",
title = "Bio-Inspired Stochastic Chance-Constrained Multi-Robot Task Allocation Using WSN",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0261.pdf},
url = {},
size = {},
abstract = {The multi-robot task allocation (MRTA) especially in unknown complex environment is one of the fundamental problems, a mostly important object in research of multi-robot. The MRTA problem is initially formulated as a chance-constrained optimisation problem. Monte Carlo simulation is used to verify the accuracy of the solution provided by the algorithm. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used. A hybrid intelligent algorithm combined Monte Carlo simulation and neural network is used for solving stochastic chance constrained models of MRTA. A practical implementation with real WSN and real mobile robots were carried out. In environment the successful implementation of tasks without collision validates the efficiency, stability and accuracy of the proposed algorithm. The convergence curve shows that as iterative generation grows, the utility increases and finally reaches a stable and optimal value. Results show that using sensor information fusion can greatly improve the efficiency. The algorithm is proved better than tradition algorithms without WSN for MRTA in real time. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhibin:2008:ijcnn,
author = "Liu Zhibin and Jin Lianwen",
title = "LATTICESVM A New Method for Multi-class Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0262.pdf},
url = {},
size = {},
abstract = {Multi-class approaches for SVM (Support Vector Machines) is a very important issue for solving many practical problems (such as OCR and face recognition), since SVM was originally designed for binary class classification. Lots of methods based on traditional binary SVM have been proposed, each with its advantages and disadvantages. Among them, one-versus-one, one-versus-all, directed acyclic graph and binary tree are four most widely used methods. In this paper a novel LATTICESVM method, which can significantly reduce the storage and computational complexity, is proposed for multi-class SVM. A comparison in terms of storage, classification speed and accuracy against the four traditional multi-class approaches is given through both theoretic analysis and experiments on large scale handwritten Chinese character recognition. The results obtained clearly show the effectiveness of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xiang:2008:ijcnn,
author = "Kui Xiang and Xixiu Wu and Jian Fu and Jing Chen ",
title = "Input-Output Model of Time Series Based on ESN",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0263.pdf},
url = {},
size = {},
abstract = {Echo state networks (ESN) is a novel time series model stemming from RNN. The reservoir of ESN provides a rich set of dynamics whose weighted combination can approximate teacher signal effectively. Its excellent predicting capability in deterministic system has been proved by several benchmarks. Yet analysing an input-output system using ESN has not discussed. In the paper a new I/O model is presented to address both input and output series as the observation of systems which comprise a teacher vector. Learning the vector by ESN can establish the mapping from input to output and predict the system output on the basis of new input. Though learning only the output series can also predict the unknown quantity, repeating simulations demonstrate that our model can restrain the instability of network state and improve the predicting performance. Such model gives us new choice to analyse input-output system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hidalgo:2008:ijcnn,
author = "Denisse Hidalgo and Oscar Castillo and Patricia Melin",
title = "Optimization with Genetic Algorithms of Modular Neural Networks Using Interval Type-2 Fuzzy Logic for Response Integration: The Case of Multimodal Biometry",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0266.pdf},
url = {},
size = {},
abstract = {We describe in this paper a comparative study of Fuzzy Inference Systems as methods of integration in modular neural networks (MNN's) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimised with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimised fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behaviour of the two different integration methods of modular neural networks for multimodal biometry. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bai:2008:ijcnn,
author = "Xue Bai and Vladimir Cherkassky",
title = "Gender Classification of Human Faces Using Inference Through Contradictions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0269.pdf},
url = {},
size = {},
abstract = {We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in [9]. This approach allows to incorporate a priori knowledge in the form of additional (unlabelled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this approach enables better generalisation over standard SVM classification (using labeled data alone). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang3:2008:ijcnn,
author = "Liyan Zhang and Shuhai Quan and Kui Xiang ",
title = "Recurrent Neural Network Optimization for Model Predictive Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0270.pdf},
url = {},
size = {},
abstract = {High computational burden in solving quadratic programming problem is a major obstacle when we apply model predictive control to industrial process. Recurrent neural networks offer a new quadratic programming optimisation approach due to its parallel computational performance. In this paper, we present a new architecture of solving model predictive control (MPC) problem based on one layer recurrent neural network. We give algorithm of model predictive control based on recurrent neural network and prove convergence property of one layer recurrent neural network at each sample step. Two examples demonstrate the effectiveness and efficient of the proposed recurrent neural network based MPC. Simulation results show that this approach can use fast converge property and the parallel computation ability of recurrent neural network and apply to real-time industrial process control. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Han2:2008:ijcnn,
author = "Min Han and Ru Wei and Decai Li ",
title = "Multivariate Chaotic Time Series Analysis and Prediction Using Improved Nonlinear Canonical Correlation Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0271.pdf},
url = {},
size = {},
abstract = {This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural network. Then linear CCA is performed using the transformed nonlinear data sets, which corresponds to make nonlinear CCA of the original data. A modified cost function of the neural network with Lagrange multipliers and a joint learning rule based on gradient ascent algorithm which maximises the correlation coefficient of the network outputs is used to extract the maximal correlation pattern between the input and output of a prediction model. Finally, a regression model is constructed to implement the prediction problem. The performance of RBFCCA prediction algorithm is demonstrated via the prediction problem of Lorenz time series and some practical observed time series. The results compared with the traditional neural network method and the KCCA method indicate that the RBFCCA algorithm proposed in this paper is able to capture the dynamics of complex systems and give reliable prediction accuracy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo3:2008:ijcnn,
author = "Chen Guo and Campbell Wilson",
title = "Use of Self-Organizing Maps for Texture Feature Selection in Content-Based Image Retrieval",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0273.pdf},
url = {},
size = {},
abstract = {The ``Semantic Gap'' observed in content-based image retrieval (CBIR) has become a highly active research topic in last twenty years, and it is widely accepted that domain specification is one of the most effective methods of addressing this problem. However, along with the challenge of making a CBIR system specific to a particular domain comes the challenge of making those features object dependent. Independent Component Analysis (ICA) is a powerful tool for detecting underlying texture features in images. However, features detected in this way often contain groups of features which are essentially shifted or rotated versions of each other. Thus, a method of dimensionality reduction that takes this self-similarity into account is required. In this paper, we proposed a Self-Organising Map (SOM) based clustering method to reduce the dimensionality of feature space. This method comprises two phases: clustering as well as representative selection. The result of the implementation confirms this method offers effective CBIR dimensionality reduction when using the ICA method of texture feature extraction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Han3:2008:ijcnn,
author = "Min Han and Xinzhe Wang and Yijie Wang",
title = "Applying ICA on Neural Network to Simplify BOF Endpiont Predicting Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0274.pdf},
url = {},
size = {},
abstract = {This paper proposes an improved method to modelling the dynamic process of basic oxygen furnace (BOF) and the main idea is simplification. Aiming at the problem that it is usually difficult to build a precise endpoint dynamic model because of the high dimensional input variables which affect the final results - carbon content and temperature, this paper builds endpoint carbon content prediction model and endpoint temperature prediction model separately. First, the more important variables are chosen for two models by analysing the mechanism. The independent component analysis (ICA) is applied to reduce the input dimension for temperature prediction model. Results show that the model simplification is essential and effective. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu5:2008:ijcnn,
author = "Gang Xu and Yuqing Lei",
title = "A New Image Recognition Algorithm Based on Skeleton",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0276.pdf},
url = {},
size = {},
abstract = {Traditional recognition methods which mainly match object images with their skeleton couldn't resolve well complex objects' recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of object main skeleton and its extraction method is described, and with view to the characteristics of the skeleton, an extended Hu moment invariants algorithm is brought forward to calculate moment invariants of the skeleton. At the recognition stage, a two-layer generalised regression radial Basis (RBF) neural network is adopted to do machine self-learning and target- identifying. Compared with the present recognition methods based on similarity matching with skeleton, the algorithm doesn't need to face many problems such as the difficulties in matching and realising based on skeleton graph, the complexity of the Shock Graphs, the object selectivity of the Reeb Graphs and the order of the nodes which can't be guaranteed in SA-tree and so on. Compared with traditional moment recognition methods, the method not only can make calculation results meet scale, translation and rotation invariance, but also can reduce the number of related efficient pixels during moment calculation. In the meanwhile, it overcomes the difficulties that traditional moment recognition methods encountered when they deal with the fuzzy object boundary, and thus is effective. Finally, some experiments prove that the algorithm has better results for general object recognition. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Han4:2008:ijcnn,
author = "Min Han and Jia Yin and Yang Li",
title = "The Learning Algorithm Based on Multiresolution Analysis for Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0278.pdf},
url = {},
size = {},
abstract = {The multiresolution analysis learning algorithm (MRAL) for neural networks is proposed to get a more precious model from the noisy data set, which based on Multi-resolution Analysis (MRA) of the wavelet transformation and nondominated sorting genetic algorithm-II (NSGA-II). Several different scaled signals of the error function are used as the objections, and NSGA-II algorithm is applied to optimise this multiobjective problem. The new algorithm can improve the study ability of the neural networks. Two examples are provided to illustrate the efficiency of the MRAL algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sandberg:2008:ijcnn,
author = "David Sandberg and Mattias Wahde",
title = "Particle Swarm Optimization of Feedforward Neural Networks for the Detection of Drowsy Driving",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0288.pdf},
url = {},
size = {},
abstract = {The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behaviour. Artificial neural networks, trained using particle swarm optimisation, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute. The neural networks obtained outperform the best individual indicators by a few percentage points, the best network reaching a performance (average of sensitivity and specificity) of around 75percent on previously unseen test data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nguwi:2008:ijcnn,
author = "Yok-Yen Nguwi and Siu-Yeung Cho",
title = "Two-Tier Self-Organizing Visual Model for Road Sign Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0289.pdf},
url = {},
size = {},
abstract = {This paper attempts to model human brain's cognitive process at the primary visual cortex to comprehend road sign. The cortical maps in visual cortex have been widely focused in recent research. We propose a visual model that locates road sign in an image and identifies the localised road sign. Gabor wavelets are used to encode visual information and extract features. Self-organizing maps are used to cluster and classify the road sign images. We evaluate the system with various test sets. The experimental results show encouraging recognition hit rates. There are quite a number of literatures [1]-[13] introducing different approaches to classify road sign, but none has adopted unsupervised approach. This work makes use of two-tier topological maps to recognise road signs. First-tier map, called detecting map, filters out non-road sign images and regions. Second-tier map, called recognizing map, classifies a road sign into appropriate class. },
keywords = { Self-Organizing Map, Gabor feature, Visual Model, road sign recognition },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zheng:2008:ijcnn,
author = "Lei Zheng and Siu-Yeung Cho and Chai Quek ",
title = "A Memory-Based Reinforcement Learning Algorithm for Partially Observable Markovian Decision Processes",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0291.pdf},
url = {},
size = {},
abstract = {This paper presents a modified version of U-Tree [1], a memory-based reinforcement learning (RL) algorithm that uses selective perception and short-term memory to handle partially observable Markovian decision processes (POMDP). Conventional RL algorithms rely on a set of pre-defined states to model the environment, even though it can learn the state transitions from experience. U-Tree is not only able to do that, it can also build the state model by itself based on raw sensor inputs. This paper enhances U-Tree's model generation process. The paper also shows that because of the simplified and yet effective state model generated by U-Tree, it is feasible and preferable to adopt the classical Dynamic Programming (DP) algorithm for average reward MDP to solve some difficult POMDP problems. The new U-Tree is tested using a car-driving task with 31,224 world states, with the agent having very limited sensory information and little knowledge about the dynamics of the environment. },
keywords = { Reinforcement Learning Algorithm, Partially Observable Markovian Decision Processes, Dynamic Programming, Average Reward.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Uda:2008:ijcnn,
author = "Yoichi Uda and Yuko Osana",
title = "Knowledge Processing System Using Kohonen Feature Map Associative Memory with Refractoriness Based on Area Representation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0292.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a knowledge processing system using Kohonen feature map associative memory with refractoriness based on area representation. The proposed system is based on the Kohonen feature map associative memory with refractoriness based on area representation. In the conventional Kohonen feature map associative memory, only one-to-one associations can be realised. In contrast, one-tomany associations are realised by the refractoriness of neurons in the Map Layer in the Kohonen feature map associative memory with refractoriness based on area representation. In this research, the Kohonen feature map associative memory with refractoriness based on area representation is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed system has the following features: (1) it can deal with the knowledge which is represented in a form of semantic network; (2) it can deal with characteristics inheritance; (3) it is robust for noisy input. We carried out a series of computer experiment and confirmed the effectiveness of the proposed system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tanimizu:2008:ijcnn,
author = "Hiroyuki Tanimizu and Yuko Osana",
title = "Similarity-Based Image Retrieval from Plural Key Images by Self-Organizing Map with Refractoriness",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0295.pdf},
url = {},
size = {},
abstract = {In this research, we propose a similarity-based image retrieval from plural key images by self-organising map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The proposed image retrieval system from plural key images using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only colour information but also spectrum, impression words and keywords are employed. In the proposed system, the similarity-based image retrieval from plural key images can be realised. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shiratori:2008:ijcnn,
author = "Tomonori Shiratori and Yuko Osana",
title = "Kohonen Feature Map Associative Memory with Area Representation for Sequential Analog Patterns",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0297.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the Kohonen feature map associative memory with area representation for sequential patterns. Although the conventional Kohonen feature map associative memory with area representation for sequential patterns can deal with only binary (bipolar) patterns, the proposed model can deal not only binary (bipolar) patterns but also analog patterns. The proposed model can learn sequential analog patterns successively, and has robustness for damaged neurons. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hirata:2008:ijcnn,
author = "Takanori Hirata and Takuya Tokuda and Yuko Osana",
title = "Melody Retrieval by Self-Organizing Map with Refractoriness",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0300.pdf},
url = {},
size = {},
abstract = {In this research, we propose a similarity-based melody retrieval by self-organising map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The proposed melody retrieval system using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar melodies. In this melody retrieval system, as the melody features, tone, rhythm and keyword (genre of music) are employed. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chan:2008:ijcnn,
author = "Chien-Lung Chan and Yu-Chen Liu and Shih-Hui Luo",
title = "Investigation of Diabetic Microvascular Complications Using Data Mining Techniques",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0307.pdf},
url = {},
size = {},
abstract = {This study theoretically analyses and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared. Results: It is found that Creatinine is the most important predictor for diabetic retinopathy. If Creatinine is out of control, diabetic patients will easily suffer from diabetic retinopathy in spite of many other laboratory evaluations are normal. The sensitivity and specificity for diabetic retinopathy prediction using C5.0 are 58.62 and 74.73, and those using neural network are 59.48 and 99.86, respectively. In addition, diabetic nephropathy will happen when several laboratory evaluation values are worse than target values. Female diabetics with diabetic family history are easier to undergo this complication. The sensitivity and specificity for diabetic nephropathy prediction using C5.0 are 69.44 and 81.36, and those using neural network are 74.44 and 98.55, respectively. For diabetic neuropathy, female diabetics feature unqualified BMI, HbA1c and AC sugar, while male diabetics mostly have uncontrolled blood pressure. Besides, smoking diabetics are more difficult to avoid this complication. The sensitivity and specificity for diabetic foot problem prediction using C5.0 are 64.71 and 83.48, and those using neural network are 67.63 and 99.70, respectively. },
keywords = { Diabetes Mellitus, Retinopathy, Nephropathy, Neuropathy, C5.0, Neural Network.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lao:2008:ijcnn,
author = "Jian Lao and Quansheng Ren and Jianye Zhao",
title = "A Novel Chaotic Stream DS-UWB System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0309.pdf},
url = {},
size = {},
abstract = {The novel Chaotic Stream DS-UWB system proposed in this paper accomplishes synchronisation, modulation and encryption of data in only one channel transmission mechanism. The architecture of the system combines the chaotic pulse position modulation, the complex chaotic stream ciphers encryption and the chaotic direct spread codes with the PAM based DS-UWB communication system. The synchronisation of the system is robust with noise and distortion. To overcome problems caused by the digital finite precision, the cipher systems are designed carefully to guarantee Shannon's three principles of secure systems. The chaotic direct spread code is changing with time just as the chaotic stream ciphers, which provides a better performance on security, spectrum and multi-access. Results of Simulations show that the Chaotic Stream DS-UWB has much better BER performances (3 ~ 5dB) than the ordinary DS-UWB when the channel is multi-path channel. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Masaru:2008:ijcnn,
author = "Fujita Masaru and Takase Haruhiko and Kita Hidehiko and Hayashi Terumine ",
title = "Shape of Error Surfaces in SpikeProp",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0310.pdf},
url = {},
size = {},
abstract = {In this paper, we discuss the shape of error surfaces, which represent error depending on parameters, in Spiking Neural Networks for SpikeProp[1]. SpikeProp is a learning algorithm that adjusts timing of spikes. The discussion is held in the viewpoint of the difference between analogue computation and digital computation (especially in discrete time). Since the error is defined by timing of spikes, quantisation error brought by digital computation changes the shape. We show typical shapes of error surfaces through some experiments. Digital computation bring rough error surfaces, which have many false local minima. These local minima will disturb effective acceleration of learning process by sophisticated algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Aly:2008:ijcnn,
author = "Saleh Aly and Naoyuki Tsuruta and Rin-Ichiro Taniguchi and Atsushi Shimada",
title = "Visual Feature Extraction Using Variable Map-Dimension Hypercolumn Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0311.pdf},
url = {},
size = {},
abstract = {Hyper-column model (HCM) is a neural network model previously proposed to solve image recognition problem. In this paper, we propose an improved version of HCM network and demonstrate its ability to solve face recognition problem. HCM network is a hierarchical model based on self-organising map (SOM) that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation. This invariance achieved by alternating between feature extraction and feature integration operation. To improve the recognition rate of HCM, we propose a variable dimension for each map in the feature extraction layer. The number of neurons in each map-side is decided automatically from training data. We demonstrate the performance of the approach using ORL face database. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fu2:2008:ijcnn,
author = "Wei Fu and Xiaodong Gu and Yuanyuan Wang ",
title = "Image Quality Assessment Using Edge and Contrast Similarity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0312.pdf},
url = {},
size = {},
abstract = {Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image and the reference(perfect) image. We demonstrate its promise through a set of intuitive examples, as well as validate its performance with subjective ratings. We also compare our method with two other state-of-the-art objective ones, which uses 550 images with different distortion types and BP neural network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chan2:2008:ijcnn,
author = "Chien-Lung Chan and Chien-Wei Chen",
title = "Discovery of Association Rules in Metabolic Syndrome Related Diseases",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0313.pdf},
url = {},
size = {},
abstract = {Since 1980, the Hypertension and Diabetes Mellitus in Metabolic Syndrome have appeared in the top ten causes of death every year in Taiwan. This research aims to study Metabolic Syndrome related disease by using data mining technique, and to understand the strength of association between Diabetes Mellitus, Hypertension and Hyperlipidemia. The data of this research came from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health. It includes the Diabetes Mellitus patients' health insurance record during 2003-2005 in Taiwan. We used association rules to find diseases patterns of Metabolic Syndrome related disease. Using data mining technique can find and confirm the relation between diseases. We found Diabetes Mellitus is related to oral diseases and blear eyes. We also found that patients with Metabolic Syndrome have higher connection with liver diseases than patients with Diabetes Mellitus. },
keywords = { Association Rules, Metabolic Syndrome.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhou:2008:ijcnn,
author = "Jingchao Zhou and Baihua Xiao and Qiudan Li",
title = "A No Reference Image Quality Assessment Method for JPEG2000",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0314.pdf},
url = {},
size = {},
abstract = {This paper presents a novel no reference method to assess image quality. Firstly, the image is divided into many blocks. Textured blocks are selected and their amplitude fall-off curves are employed for quality prediction based on natural scene statistics. Secondly, projections of wavelet coefficients between adjacent scales with the same orientation are used to measure the positional similarity. At last, general regression neural network is adopted to conduct quality prediction according to features from above two aspects. The performance of our method is evaluated on a public data set and experimental results confirm its effectiveness. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang2:2008:ijcnn,
author = "Tingwen Huang and Hui Huang",
title = "Exponential Stability of Periodic Solution of Impulsive Fuzzy BAM Neural Networks with Time-Varying Delays",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0316.pdf},
url = {},
size = {},
abstract = {In this paper, we study impulsive fuzzy BAM neural networks. Criteria are obtained for exponential stability of globally exponential stability of periodic solution of time varying delayed fuzzy neural networks with impulses.The criteria obtained in this paper is easily verifiable. It is believed that it is useful in design neural networks in practices. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen7:2008:ijcnn,
author = "Jing Chen and Guangcheng Xi",
title = "Entropy Partition Method and Its Application for Discrete Variables and Continuous Variables",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0317.pdf},
url = {},
size = {},
abstract = {Entropy partition method for complex system has been applied in many kinds of fields. In this paper, we improve the calculation of correlative measure for both discrete variables and continuous variables, and apply this method in vascular endothelial dysfunction (ED) discrete data and neuro-endocrine-immune (NEI) continuous data respectively. The partition results show this entropy partition method's broad availability and obvious advantage in dealing with complex, multiple, nonlinear data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Serrano:2008:ijcnn,
author = "J. Ignacio Serrano and M. Dolores del Castillo and Á ngel Iglesias and Jesús Oliva",
title = "Characterizing Prior Knowledge-Attention Relationship by a Computational Model of Cognitive Reading",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0318.pdf},
url = {},
size = {},
abstract = {Interest and prior knowledge are supposed to influence reading comprehension and learning from natural language texts. The effects of interest have been well studied in the literature, but little effort has been made on empirically establishing the influences of prior knowledge in reading attention and engagement, and therefore in comprehension and learning. A quantitative characterisation of this relationship is proposed in this paper by means of a connectionist and computational method, a model of cognitive reading which allows to configure and isolate inferential depth and memory issues, which are well-known to be strongly related to attention and engagement. Results have pointed out a clear and straight relationship between prior knowledge and the latter issues and they have shown the computational model to be suitable as experimental framework for the validation of further hypothesis related to human language processing. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zeng:2008:ijcnn,
author = "Zhigang Zeng and Huangqiong Chen and Shiping Wen ",
title = "Global Exponential Stability of Recurrent Neural Networks with Pure Time-Varying Delays",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0320.pdf},
url = {},
size = {},
abstract = {This paper presents some theoretical results on the global exponential stability of recurrent neural networks with pure time-varying delays. It is shown that the recurrent neural network is globally exponentially stable, if the pure time varying delays satisfy some limitations. In addition to providing new criteria for recurrent neural networks with pure time varying delays, these stability conditions also improve upon the existing ones with constant time delays and without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang4:2008:ijcnn,
author = "Xuejie Zhang and Alex Leng Phuan Tay",
title = "Neural Classification of Objects Based on Gabor Signature",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0324.pdf},
url = {},
size = {},
abstract = {This paper uses a combination of K-Iterations Fast Learning Artificial Neural Network (KFLANN) and Gabor filters to create a Gabor signature classifier. Gabor filters are known to be useful in modelling responses of the receptive fields and the properties of simple cells in the visual cortex. The responses produced by Gabor filters produce good quantifiers of the visual content in any given image. A robust edge and edge orientation detection method using a combination of antisymmetric and symmetric Gabor filters is described in detail. The edge and edge orientation information are subsequently used to construct a Gabor signature that is size and orientation invariant. Some experimental results are provided to present the effectiveness and robustness of this signature construction for object classification. In addition to the KFLANN implementation, results were also obtained from a nearest neighbor classifier, back-propagation neural network and k-means clustering for the purposes of comparison. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu5:2008:ijcnn,
author = "Zhijun Yu and Jianming Wei and Haitao Liu ",
title = "A New Adaptive Maneuvering Target Tracking Algorithm Using Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0325.pdf},
url = {},
size = {},
abstract = {A new neural network (NN) aided adaptive unscented Kalman filter (UKF) is presented for tracking high maneuvering target. In practice, the dynamic systems of many target tracking problems are usually nonlinear and incompletely observed, moreover, there may be large modeling errors when the target is maneuverable or some parameters of the system models are inaccurate or incorrect. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. On the other hand, some nonlinear filtering methods such as extended Kalman filter (EKF) have been used to train a NN with fast convergence speed by augmenting the state with unknown connecting weights. Tackling the natural coalescent between the filtering algorithm and the NN described above, first a more efficient learning algorithm based on unscented Kalman filter (UKF) is derived, which can give a more accurate estimate of the weights and possess faster convergence rate. We then extend the algorithm to form a new NN aided adaptive UKF algorithm and use it in maneuvering target tracking applications. The NN in this algorithm is used to approximate the uncertainty of system models and is trained online, together with the target state estimation. Some simulations are also given to validate that the proposed method can give well state estimation of a highly maneuvering target. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kretinin:2008:ijcnn,
author = "A. V. Kretinin and Yu. A. Bulygin and S. G. Valyuhov",
title = "Intelligent Algorithm for Forecasting of Optimum Neurons Quantity in Perceptron with One Hidden Layer",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0327.pdf},
url = {},
size = {},
abstract = {The research performed focus on the development of methods of building-up of the intelligent neural network modeling solutions database as well as methods of approximation aiming at empirical knowledge conservation and representation to find the best structure of the artificial neural network (ANN). The learning sample is made up of solutions of approximation of one-dimensional functions defined in the uniform grid nodes with the help of perceptrontype ANN with one hidden layer (single-layer perceptron-SLP). Computational experiment plan is made up of the points with uniform grid nodes abscissas, and the ordinates are defined by means of using of Sobol-Statnikov generator of the semi-uniform sequence of numbers. The training uses the stochastic approximation algorithm that is a modification of the back propagation algorithm. As a result of SLP given points training the minimum number of neurons in the hidden layer is defined at which the target accuracy is achieved. Numerous solutions of neural network approximations of one-dimensional functions of different topology are used to build-up neural network database to determine the best neuron number in the hidden layer of single-layer perceptron in order to attain the required approximation quality. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Abdel-Gawad:2008:ijcnn,
author = "Ahmed H. Abdel-Gawad and Amir F. Atiya",
title = "A New Accurate Approximation for the Gaussian Process Classification Problem",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0328.pdf},
url = {},
size = {},
abstract = {Gaussian processes is a very promising novel technology that has been applied for both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification case. The reason is that we encounter intractable integrals. In this paper we propose a new approximate solution for the Gaussian process classification problem. The approximating formula is based on certain transformations of the variables and manipulations that lead to orthant multivariate Gaussian integrals. An approximation is then applied that leads to a very simple formula. In spite of its simplicity, the formula gives better results in terms of classification accuracy and speed compared to some of the well-known competing methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheng2:2008:ijcnn,
author = "Wen-Chang Cheng ",
title = "3D Human Face Reconstruction with Three Images Based on Constrained ICA",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0330.pdf},
url = {},
size = {},
abstract = {In this paper, we propose an improved photometric stereo scheme based on the Lambertian reflectance model and the constrained independent component analysis (CICA) method. When we obtain the object's surface normal vector on each point of an image by ICA model to reconstruct 3-D shape, we will find the normal vector's coordinates whose x-axis, y-axis and z-axis value are not arranged in turn. So we use CICA method to solve the problem. Then we obtain correct normal vector's sequence form surface, and using the enforcing integrability method to reconstruct 3-D object. Finally, we test our algorithm on a number of real images captured from the Yale Face Database B. The experimental results demonstrate that the proposed CICA method is work to find the order of normal vector. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mu2:2008:ijcnn,
author = "Chaoxu Mu and Hua Liang and Changyin Sun",
title = "Inverse System Identification of Nonlinear Systems Using Least Square Support Vector Machine Based on FCM Clustering",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0331.pdf},
url = {},
size = {},
abstract = {The algorithm of least square support vector machine (LSSVM) based on fuzzy c-means (FCM) clustering is presented in this paper, which can select the number of clusters automatically depending on different parameters and samples. We adopt the method to identify the inverse system with crucial spanless process variables and the inenarrable nonlinear character. In the course of identification, we construct the allied inverse system by the left inverse soft-sensing function and the right inverse system, then use the proposed method to approach the nonlinear allied inverse system via offline training. Simulation experiments are performed and indicate that the proposed method is effective and provides satisfactory performance with excellent accuracy and low computational cost comparing with the conventional method using LSSVM. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang3:2008:ijcnn,
author = "Wenlu Yang and Liqing Zhang",
title = "Spatiotemporal Feature Extraction Based on Invariance Representation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0333.pdf},
url = {},
size = {},
abstract = {This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functions of the visual cortex. We propose a novel hierarchical model based on invariance and independent component analysis for spatiotemporal feature extraction. Training the model from patches sampled from natural scenes, we can obtain image basis with properties of translational, scaling, and rotational features. Further experiments on TV videos and facial image sequences show different characteristics of spatiotemporal features are achieved by training the proposed model. All these computer simulations verify that our proposed model is successful for spatiotemporal feature extraction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hassan:2008:ijcnn,
author = "Mostafa M. Hassan and Amir F. Atiya",
title = "A New Multidimensional Penalized Likelihood Regression Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0334.pdf},
url = {},
size = {},
abstract = {Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in the statistics literature since the seventies, has focused mostly on the onedimensional case. Attempts to consider the general multidimensional case have been limited. In this paper we propose a new multidimensional penalized likelihood regression method. The approach is based on proposing a roughness term based on the discrepancy between the function values among the Knearest- neighbors. The proposed formulation yields a simple solution in terms of a system of linear equations. We also derive an iterative solution to the problem that sheds light on its basic functionality. The iteration consists of repeatedly taking the weighted average of the target output value and the estimated function values of the K-nearest-neighbors. We show that the proposed model is fairly versatile in that it exhibits nice features in handling user-defined function constraints and data imperfections. Experimental results confirm that it is competitive with the Gaussian process regression method (one of the best methods out there), and exhibits significant speed advantage. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shimada:2008:ijcnn,
author = "Atsushi Shimada and Madoka Kanouchi and Daisaku Arita and Rin-ichiro Taniguchi",
title = "Robust Estimation of Human Posture Using Incremental Learnable Self-Organizing Map",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0335.pdf},
url = {},
size = {},
abstract = {We propose an approach to improve the accuracy of estimating feature points of human body on a vision-based motion capture system (MICS) by using the Variable-density Self Organizing Map (VDSOM). The VDSOM is a kind of Self Organizing Map (SOM) and has an ability to learn training samples incrementally. We let VDSOM learn 3-D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3-D feature point could not be estimated correctly, we use the VDSOM for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. We use this ability dto recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tang2:2008:ijcnn,
author = "Yaohua Tang and Jinghuai Gao and Guangzhao Cui",
title = "Feature Selection Based on Kernel Pattern Similarity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0336.pdf},
url = {},
size = {},
abstract = {Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern similarity measurement in kernel space, class separability is deduced and we explore the use of the class separability in feature selection, The key idea of our method is that the feature whose removal downgrades the class separability in kernel space most is relevance to the classification. Experiments on linear and nonlinear synthetic problems and real world data sets have been carried out to demonstrate the effectiveness of this method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mammone:2008:ijcnn,
author = "Nadia Mammone and Fabio La Foresta and Mario Versaci and Umberto Aguglia",
title = "Mutual Information for Measuring Independence of STLmax Time Series in the Epileptic Brain",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0338.pdf},
url = {},
size = {},
abstract = {Results in literature show that the convergence of the Short-Term Maximum Lyapunov Exponent (STLmax) time series, extracted from intracranial EEG recorded from patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. When the STLmax profiles of different electrode sites converge (high entrainment) a seizure is likely to occur. In this paper Renyi's Mutual information (MI) is introduced in order to investigate the independence between pairs of electrodes involved in the epileptogenesis. A scalp EEG recording and an intracranial EEG recording, including two seizures each, were analysed. STLmax was estimated for each critical electrode and then MI between couples of STLmax profiles was measured. MI showed sudden spikes that occurred 8 to 15 min before the seizure onset. Thus seizure onset appears related to a burst in MI: this suggests that seizure development might restore the independence between STLmax of critical electrode sites. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Velde:2008:ijcnn,
author = "Frank van der Velde and Marc de Kamps",
title = "A Neural Architecture for Grounded Cognition: Representation, Structure, Dynamics and Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0339.pdf},
url = {},
size = {},
abstract = {Human cognition is characterised by three important features: productivity, dynamics and grounding. These features can be integrated in a neural architecture. The representations in this architecture are not symbol tokens, that can be copied and transported. Instead, the representations always remain "in situ", because they are grounded in perception, action, emotion, associations and (semantic) relations. The neural architecture shows how these representations can be combined in a productive manner, and how dynamics influences this process. The constraints that each of these features impose on each other could result in an architecture in which the local and the global aspects of cognition interact in processing and learning. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fieres:2008:ijcnn,
author = "Johannes Fieres and Johannes Schemmel",
title = "Realizing Biological Spiking Network Models in a Configurable Wafer-Scale Hardware System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0342.pdf},
url = {},
size = {},
abstract = {An analog VLSI hardware architecture for the distributed simulation of large-scale spiking neural networks has been developed. Several hundred integrated computing nodes, each hosting up to 512 neurons, will be interconnected and operated on un-cut silicon wafers. The electro-technical aspects and the details of the hardware implementation are covered in a separate contribution to this conference. This paper focuses on the usability of the system by demonstrating that biologically relevant network models can in fact be mapped to this system. Different network configurations are established on the hardware by programmable switch matrices, repeaters, and address decoders. Systematic routing algorithms are presented to map a given network model to the hardware system. Routing is simulated for several network examples, proving the system's practical applicability. Furthermore, the routing simulations are used to fix values for yet open hardware parameters. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Beigi:2008:ijcnn,
author = "Majid M. Beigi and Andreas Zell",
title = "FIR-Based Classifiers for Animal Behavior Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0343.pdf},
url = {},
size = {},
abstract = {In this paper, we implement a new method for classification of biological signals in general, and use it in the animal behavior classification as an example. The forced swimming test of rats or mice is a frequently used behavioral test to evaluate the efficacy of drugs in rats or mice. Frequently used features for that evaluation are obtained through observing three states: immobility, struggling/climbing and swimming in activity profiles.We consider that those activity profiles (signals) inherently contain undesired and interference noise that should be removed before feature extraction and classification. We use a Finite Impulse Response (FIR) filter to filter out that additive noise from the activity profile. The parameters of the FIR filter are obtained via maximizing the accuracy of a classifier that tries to make a discrimination between two classes of the activity profiles (e.g. drug vs. control). We use the kernel Fisher discriminant criterion as a criterion for the discrimination, the DIviding RECTangles (DIRECT) search method for solving the optimization problem and Support Vector Machines (SVMs) for the classification task. We show that Autoregressive (AR) coefficients are suitable features for the extraction of the dynamic behavior of rats and also the classification of activity profiles. Our proposed behavior classification method provides a reliable discrimination of different classes of antidepressant drugs (imipramine and desipramine) administered to rats versus a vehicle-treated group. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alba:2008:ijcnn,
author = "Enrique Alba and Davide Anguita and Alessandro Ghio and Sandro Ridella",
title = "Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0344.pdf},
url = {},
size = {},
abstract = {In this work we show that a metaheuristic, the Variable Neighborhood Search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the Support Vector Machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource- limited hardware devices, such as Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs). The proposal has been tested on a machine-vision benchmark dataset for embedded automotive applications, showing considerable performance improvements respect to previously used techniques. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tamminen:2008:ijcnn,
author = "Satu Tamminen and Ilmari Juutilainen and Juha Roning ",
title = "Product Design Model for Impact Toughness Estimation in Steel Plate Manufacturing",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0346.pdf},
url = {},
size = {},
abstract = {The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost valuable information. The method is suitable for all steel grades in production and it is not restricted to a few test temperatures. There were differences between the performances of different product groups, but overall performance was promising. Next the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jain:2008:ijcnn,
author = "Brijnesh Jain and Klaus Obermayer ",
title = "On the Sample Mean of Graphs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0347.pdf},
url = {},
size = {},
abstract = {We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sanchez:2008:ijcnn,
author = "E. N. Sanchez and E. A. Hernandez and C. Cadet",
title = "Discrete-Time Recurrent High Order Neural Observer for Activated Sludge Wastewater Treatment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0350.pdf},
url = {},
size = {},
abstract = {This paper presents a recurrent neural observer to estimate substrate and biomass concentrations in an activated sludge waste water treatment. The observer is based on a discrete time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. This observer is then associated with a hybrid intelligent system to control the substrate/biomass concentration ratio. The neural observer performance is illustrated via simulations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Greer:2008:ijcnn,
author = "Douglas S. Greer ",
title = "Stable Reciprocal Image Associations in Cognitive Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0352.pdf},
url = {},
size = {},
abstract = {Sensory inputs such as visual images or audio spectrograms can act as symbols in a new cognitive model. The stability of direct image association operators allows the discrete bit patterns in a general-purpose symbol processing system to be replaced with continuous real-world signals. Analogous to an SR flip-flop, two reciprocal images recursively connected by association processors, can ``lock'' each other in place. A computational model of the Brodmann areas, whose boundaries are defined by the thickness of the exterior and interior lamina in cerebral cortex, closely resembles this structure. The recurrence between the cells in the cortical columns allows local connections in small regions to form overall global image associations. An implementation, based on neurotransmitter field theory, demonstrates the stability of the reciprocal-image attractors. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alanis:2008:ijcnn,
author = "Alma Y. Alanis and Edgar N. Sanchez",
title = "Real-Time Discrete Recurrent High Order Neural Observer for Induction Motors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0355.pdf},
url = {},
size = {},
abstract = {A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability real-time results are included. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huemer:2008:ijcnn,
author = "Andreas Huemer and Mario Gongora and David Elizondo",
title = "Evolving a Neural Network Using Dyadic Connections",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0356.pdf},
url = {},
size = {},
abstract = {Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by an expert.We present a system where the processing of the feedback is integrated entirely in the growing of a spiking neural network system. The feedback is extracted from a measurement of a reward interpretation system provided by the designer, which takes into consideration the robot actions without the need for external explicit inputs.Starting with a small basic neural network, new connections are created. The connections are separated into artificial dendrites, which are mainly used for classification issues, and artificial axons, which are responsible for selecting appropriate actions. New neurons are then created using a special connection structure and the current reward interpretation of the robot.We show that dyadic connections can also make an artificial neural network acting and learning faster because they reduce the total number of neurons and connections needed in the resulting neural system.The main contribution of this research is the creation of a novel unsupervised learning system where the designer needs to define only the interface between the robot and the neural network in addition to the feedback system which includes a calculation of a reward value depending on the performance of the robot (or task aim of the system being developed). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lindgren2:2008:ijcnn,
author = "Jussi T. Lindgren and Aapo Hyvärinen",
title = "On the Learning of Nonlinear Visual Features from Natural Images by Optimizing Response Energies",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0357.pdf},
url = {},
size = {},
abstract = {The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual data by optimising for robust alternative of variance of the nonlinear model outputs. By a simple transformation of the learnt model, we demonstrate that on natural images, both minimisation and maximisation in our setting lead to oriented, band-pass and localised linear filters whose responses are then nonlinearly combined. In minimisation, the method learns to multiply the responses of two Gabor-like filters, whereas in maximization it learns to subtract the response magnitudes of two Gabor-like filters. Empirically, these learnt nonlinear filters appear to function as conjunction detectors and as opponent orientation filters, respectively. We provide a preliminary explanation for our results in terms of filter energy correlations and fourth power optimisation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Achananuparp:2008:ijcnn,
author = "Palakorn Achananuparp and Xiaohua Zhou and Xiaohua Hu and Xiaodan Zhang",
title = "Semantic Representation in Text Classification Using Topic Signature Mapping",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0360.pdf},
url = {},
size = {},
abstract = {Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although it's a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption. Many researchers have attempted to address this issue by incorporating semantic information into document representation. In this paper, we study the effect of semantic representation on the effectiveness of text classification systems. We employed a novel semantic smoothing technique to derive semantic information in a form of mapping probability between topic signatures and single-word features. Two classifiers, NaÏve Bayes and Support Vector Machine, were selected to carry out the classification experiments. Overall, our topic-signature semantic representation approaches significantly outperformed traditional bag-of-word representation in most datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang3:2008:ijcnn,
author = "Zhisong Wang and Alexander Maier",
title = "Single-Trial Bistable Perception Classification Based on Sparse Nonnegative Tensor Decomposition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0361.pdf},
url = {},
size = {},
abstract = {The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper we apply a sparse nonnegative tensor factorisation (NTF) based method to extract features from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-frommotion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of intracortical LFP data collected from the middle temporal area (MT) in a macaque monkey performing a SFM task. The advantages of the sparse NTF based feature extraction approach lies in its capability to yield components common across the space, time and frequency domains and at the same time discriminative across different conditions without prior knowledge of the discriminative frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVM) classifier based on the features of the NTF components to decode the reported perception on a single-trial basis. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Iftekharuddin:2008:ijcnn,
author = "Khan M. Iftekharuddin and Yaqin Li ",
title = "A Biologically-Inspired Computational Model for Transformation Invariant Target Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0362.pdf},
url = {},
size = {},
abstract = {Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medicalpractices, geographic scene analysis, and many others. One of theprimary challenges is detection and recognition of objects in thepresence of transformations such as resolution, rotation,translation, scale and occlusion. In this work, we investigate abiologically-inspired computational modeling approach thatexploits reinforcement learning (RL) for transformationinvariantimage recognition. The RL is implemented in anadaptive critic design (ACD) framework to approximate theneuro-dynamic programming. Two ACD algorithms such asHeuristic Dynamic Programming (HDP) and Dual Heuristicdynamic Programming (DHP) are investigated and compared fortransformation invariant recognition. The two learningalgorithms are evaluated statistically using simulatedtransformations in 2-D images as well as with a large-scaleUMIST 2-D face database with pose variations. Our simulationsshow promising results for both HDP and DHP fortransformation-invariant image recognition as well as faceauthentication. Comparing the two algorithms, DHPoutperforms HDP in learning capability, as DHP takes fewersteps to perform a successful recognition task in general. On theother hand, HDP is more robust than DHP as far as success rateacross the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Rosselló:2008:ijcnn,
author = "Jose L. Rosselló and Vincent Canals and Ivan de Paul and Jaume Segura ",
title = "Using Stochastic Logic for Efficient Pattern Recognition Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0363.pdf},
url = {},
size = {},
abstract = {We present a pattern recognition methodology based on stochastic logic. The technique implements a parallel comparison of input data from a set of sensors to various prestored categories. Smart pulse-based stochastic-logic blocks are constructed to provide an efficient architecture that is able to implement Bayesian techniques, thus providing a low-cost solution in terms of gate count and power dissipation. The proposed architecture is applied to a specific navigation problem demonstrating that the system provides an almost optimal solution. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu5:2008:ijcnn,
author = "Weifeng Liu and Jose C. Príncipe",
title = "The Wellposedness Analysis of the Kernel Adaline",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0364.pdf},
url = {},
size = {},
abstract = { In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularisation as long as the training is properly early-stopped. Along with other popular regularisation techniques, this result is investigated in a unifying regularization-function concept. This understanding provides an alternative and possibly simpler way to obtain regularised solutions comparing with the cross-validation approach in regularization networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lassez:2008:ijcnn,
author = "Jean-Louis Lassez and Ryan Rossi and Stephen Sheel and Srinivas Mukkamala",
title = "Signature Based Intrusion Detection Using Latent Semantic Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0365.pdf},
url = {},
size = {},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liang:2008:ijcnn,
author = "Fengmei Liang and Keming Xie ",
title = "Classified Image Interpolation Using Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0367.pdf},
url = {},
size = {},
abstract = {An improved classified image interpolation algorithm is presented. The algorithm obtains high-resolution pixels by filtering with parameters that are optimal for the selected class. By applying the highly flexible neural network model in the proposed algorithms, classified image data is extended into a nonlinear model in each class while enhancing the sharpness and edge characteristic. Meantime the interpolation performance is improved and computer complexity is reduced. Besides emulation, the technology has been applied to the visual presenter with low-resolution image sensor. Results demonstrate that the new algorithm improves substantially the subjective and objective quality of the interpolated images over original interpolation algorithms, and meets the requirements of real time image processing. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gordon:2008:ijcnn,
author = "V. Scott Gordon ",
title = "Neighbor Annealing for Neural Network Training",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0368.pdf},
url = {},
size = {},
abstract = {An extremely simple technique for training the weights of a feedforward multilayer neural network is described and tested. The method, dubbed ``neighbor annealing'' is a simple random walk through weight space with a gradually decreasing step size. The approach is compared against backpropagation and particle swarm optimization on a variety of training tasks. Neighbor annealing is shown to perform as well or better on the test suite, and is also shown to have pragmatic advantages. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gordon2:2008:ijcnn,
author = "V. Scott Gordon and Jeb Crouson",
title = "Self-Splitting Modular Neural Network — Domain Partitioning at Boundaries of Trained Regions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0370.pdf},
url = {},
size = {},
abstract = {A modular neural network works by dividing the input domain into segments, assigning a separate neural network to each sub-domain. This paper introduces the self-splitting modular neural network, in which the partitioning of the input domain occurs during training. It works by first attempting to solve a problem with a single network. If that fails, it finds the largest chunk of the input domain that was successfully solved, and sets that aside. The remaining unsolved portion(s) of the input domain are then recursively solved according to the same strategy. Using standard back-propagation, several large problems are shown to be solved quickly and with excellent generalisation, with very little tuning, using this divide-and-conquer approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu6:2008:ijcnn,
author = "Chenfeng Xu and Jian Yang and Hongsheng Xi and Qi Jiang and Baoqun Yin ",
title = "Event-Related Optimization for a Class of Resource Location with Admission Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0372.pdf},
url = {},
size = {},
abstract = {A class of resource location service for distributed VoD system, which combines one-hop k-random walk and global centralised indexing service, is studied. First, in order to minimising the cost of communication and guaranteeing the response time performance, a Markov model is proposed to describe the queue phenomenon, admission control and the process of location. In this model, control is related with not only states but also events, which introduce more information as the control basis. Then, an optimisation algorithm that combines policy gradient estimation and stochastic approximation is proposed. This algorithm can deal with constraints and depend on no system parameter. Finally, an illustrative simulation is performed to demonstrate the effectiveness of model and algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang4:2008:ijcnn,
author = "Na Wang and Xia Li and Xuehui Luo",
title = "Semi-Supervised Kernel-Based Fuzzy C-Means with Pairwise Constraints",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0378.pdf},
url = {},
size = {},
abstract = {Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based fuzzy term defined by the violation of constraints is included. The proposed PCKFCM is compared with other clustering techniques on benchmark and the experimental results convince that effective use of constraints improves the performance of kernel-based clustering. As for the effect of key parameter selection and the non-linear capability, it outperforms a similar semi-supervised fuzzy clustering approach Pairwise Constrained Competitive Agglomeration (PCCA). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sun2:2008:ijcnn,
author = "Koun-Tem Sun and Chun-Huang Wang and Yi-Chun Lin and Yueh-Min Huang",
title = "Develop a Novel Technique for a Virtual Reality Environment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0379.pdf},
url = {},
size = {},
abstract = {In recent years, 3D virtual reality technology has been growing fast. The user interface of the Internet is facing a new challenge. Many organizations have begun to propose various kinds of specifications and standards for Web3D. The specification draft of VRML1.0 was proposed in 1994. In 2000, Java3D, Extensible 3D (X3D) and MPEG-4 Binary Format for Scene (BIFS) were formally included as important components of Web3D development by the Web3D Consortium. Quickly developing information science and technology have been shortening the half-life of knowledge. The time and the cost have become important factors in developing programs and software. Among all kinds of platforms and compiling devices, the portable bytecode of Java can reduce the waste caused by different platforms. Java technology is growing up, and its compression and security technology have improved in recent years. All of these aspects serve in making Java extremely competitive in the future. This research uses Java and Java3D API to develop a Web3D virtual reality learning environment, and proposes the development of techniques and praxis from four aspects; the virtual scene construction, learning history record, file compression technology, and security. Designers will be able to use this particular VR as a reference for developing Web3D virtual reality in the future. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hadi:2008:ijcnn,
author = "Ahmed S. Hadi ",
title = "Linear Block Code Decoder Using Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0382.pdf},
url = {},
size = {},
abstract = {In this paper the linear block code decoder is constructed by neural network. The neural network will be adapted for a single-bit error. Each layer of a neural network will simulate a linear block code decoder stage. The syndrome generator, the error detection, and the error correction stages of the linear block code decoder will be simulated by the proposed neural network. },
keywords = {Linear Block Code, Neural Network, Syndrome, Error detection, and Error Correction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nakano:2008:ijcnn,
author = "Hidehiro Nakano and Akihide Utani",
title = "Synchronization-Based Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks in Wireless Sensor Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0385.pdf},
url = {},
size = {},
abstract = {Wireless sensor networks (WSNs) have attracted significant interests of many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have their wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitation as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the longterm employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulsecoupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulation and discuss its development potential. In simulation experiment, the proposed scheme is compared with previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Watanabe:2008:ijcnn,
author = "Kenji Watanabe and Akinori Hidaka and Takio Kurita",
title = "Automatic Factorization of Biological Signals by Using Boltzmann Non-Negative Matrix Factorization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0386.pdf},
url = {},
size = {},
abstract = {We propose an automatic factorization method for time series signals that follow Boltzmann distribution. Generally time series signals are fitted by using a model function for each sample. To analyze many samples automatically, we have to apply a factorization method. When the energy dynamics are measured in thermal equilibrium, the energy distribution can be modeled by Boltzmann distribution law. The measured signals are factorized as the non-negative sum of the probability density function of Boltzmann distribution. If these signals are composed from several components, then they can be decomposed by using the idea of non-negative matrix factorization (NMF). In this paper, we modify the original NMF to introduce the probability density function modeled by Boltzmann distribution. Also the number of components in samples is estimated by using model selection method. We applied our proposed method to actual data that was measured by fluorescence correlation spectroscopy (FCS). The experimental results show that our method can automatically factorize the signals into the correct components. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang5:2008:ijcnn,
author = "Xue Wang and Daowei Bi and Liang Ding and Sheng Wang",
title = "Bootstrap Gaussian Process Classifiers for Rotating Machinery Anomaly Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0387.pdf},
url = {},
size = {},
abstract = {Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian process classifiers (GPCs) and bootstrap methods. GPCs are Bayesian probabilistic kernel classifiers and provide a well established Bayesian framework to determine the optimal or near optimal kernel hyper-parameters. They are largely unexplored for anomaly detection applications; consequently we take the initiatives to investigate GPCs' performance in these scenarios. Bootstrap methods are incorporated to improve GPCs' performance for small machinery anomaly samples by resampling at random. The proposed approach is evaluated on a motor testbed and wavelet packet is used to perform vibration analysis. Experiment results show bootstrap GPCs are highly effective and outperform GPCs and SVM with cross validation for anomaly detection. Moreover GPCs also prove to outperform SVM. Thus the proposed approach is promising for rotating machinery anomaly detection. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li5:2008:ijcnn,
author = "Jianwei Li and Weiyi Liu ",
title = "A Novel Heuristic Q-Learning Algorithm for Solving Stochastic Games",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0388.pdf},
url = {},
size = {},
abstract = {We solve Nash equlibrium of stochastic games using heuristic Q-learning method on "heuristic learning" + "Q-learning" under the framework of noncooperative general sum games. Determining whether a strategy Nash equilibrium exists in a stochastic game is NP-hard even if the game is finite. Therefore normal Q-learning method based on iterative learning can't solve stochastic games with larger scale. We attempt to make heuristic evaluations for the rewards of each stage game encountered during learning and improve continually the relevant heuristic Q-values in order to approach the optimal learning. Based on such thought, we proposed Multi-agent Heuristic Q-Learning (MHQL) method and proved that its correctness, convergence and acceptable solving time complexity. The experimentation shows that our method can drastically decrease inefficient and repetitive learning thus speed up convergence than iterative Q-learning. Our method can be regarded as a basic framework for general heuristic Q-learning to design better heuristic learning rules. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tao:2008:ijcnn,
author = "Chi-Chung Tao ",
title = "An Integrated Approach to Segment Mobile Commerce Market on the Train",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0389.pdf},
url = {},
size = {},
abstract = {An integrated approach based on innovation diffusion theory and lifestyle theory for customer segmentation of mobile commerce on the train using multivariate statistical analysis is proposed for Taiwan Railway Administration. Firstly, the contents of mobile commerce on the train are identified as segmentation variables and key factor facets for mobile commerce are redefined by using factor analysis. Then, the cluster analysis is used to classify customer groups which are named by analysis of variance (ANOVA) and market segmentations are described with demographic, lifestyle and train patronage variables by using cross analysis and Chi-squared independence tests. Finally, this paper discusses empirical results to provide valuable implications for better mobile commerce marketing strategies in the future. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang6:2008:ijcnn,
author = "Sheng Wang and Xue Wang and Daowei Bi and Liang Ding and Zheng You",
title = "Collaborative Statistical Learning with Rough Feature Reduction for Visual Target Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0390.pdf},
url = {},
size = {},
abstract = {To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine (SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples can be acquired, semi-supervised learning is desired to make full use of unlabeled samples. According to the restrict limitation in energy and bandwidth, the proposed algorithm incrementally implement classifier learning with the selected features from multiple sensor nodes, where rough set based feature reduction is used for retaining most of the intrinsic information. Furthermore, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. Experimental results demonstrate that the collaborative statistical learning algorithm can effectively implement target classification in WMSN. With the rough set based feature reduction, the proposed algorithm has outstanding performance in energy efficiency and time cost. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wu2:2008:ijcnn,
author = "Jianhua Wu and Qinbao Song and Junyi Shen",
title = "Missing Nominal Data Imputation Using Association Rule Based on Weighted Voting Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0391.pdf},
url = {},
size = {},
abstract = {With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted voting to impute missing data. Three databases were used to demonstrate the performance of the proposed method. Experimental results prove that our method is feasible in some databases. Moreover, the proposed method was evaluated using five classification problems with two incomplete databases. Experimental results indicate that the accuracy of classification is increased when the proposed method is applied for missing attribute values imputation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hidaka:2008:ijcnn,
author = "Akinori Hidaka and Takio Kurita",
title = "Fast Training Algorithm by Particle Swarm Optimization and Random Candidate Selection for Rectangular Feature Based Boosted Detector",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0393.pdf},
url = {},
size = {},
abstract = {Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jones' researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make baseclassifiers, and (2) selection of the best local feature. Because the number of local features becomes usually more than tens of thousands, the learning algorithm is time consuming if the two optimizations are completely performed. To omit the unnecessary redundancy of the learning, we propose fast boosting algorithms by using Particle Swarm Optimization (PSO) and random candidate selection (RCS). Proposed learning algorithm is 50 times faster than the usual Adaboost while keeping comparable classification accuracy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pontin:2008:ijcnn,
author = "David R. Pontin and Michael J. Watts and S. P. Worner",
title = "Using Multi-Layer Perceptrons to Predict the Presence of Jellyfish of the Genus Physalia at New Zealand Beaches",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0395.pdf},
url = {},
size = {},
abstract = {The apparent increase in number and magnitude of jellyfish blooms in the worlds oceans has lead to concerns over potential disruption and harm to global fishery stocks. Because of the potential harm that jellyfish populations can cause and to avoid impact it would be helpful to model jellyfish populations so that species presence or absence can be predicted. Data on the presence or absence of jellyfish of the genus Physalia was modelled using Multi-Layer Perceptrons (MLP) based on oceanographic data. Results indicated that MLP are capable of predicting the presence or absence of Physalia in two regions in New Zealand and of identifying significant biological variables. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cabanes:2008:ijcnn,
author = "Guenaël Cabanes and Younès Bennani",
title = "A Local Density-Based Simultaneous Two-Level Algorithm for Topographic Clustering",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0400.pdf},
url = {},
size = {},
abstract = {Determining the optimum number of clusters is an ill posed problem for which there is no simple way of knowing that number without a priori knowledge. The purpose of this paper is to provide a simultaneous two-level clustering algorithm based on self organizing map, called DS2L-SOM, which learn at the same time the structure of the data and its segmentation. The algorithm is based both on distance and density measures in order to accomplish a topographic clustering. An important feature of the algorithm is that the cluster number is discovered automatically. A great advantage of the proposed algorithm, compared to the common partitional clustering methods, is that it is not restricted to convex clusters but can recognize arbitrarily shaped clusters and touching clusters. The validity and the stability of this algorithm are superior to standard two-level clustering methods such as SOM+Kmeans and SOM+Hierarchical agglomerative clustering. This is demonstrated on a set of critical clustering problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guan:2008:ijcnn,
author = "Donghai Guan and Weiwei Yuan and Young-Koo Lee and Sungyoung Lee",
title = "Semi-supervised Nearest Neighbor Editing",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0405.pdf},
url = {},
size = {},
abstract = {This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data editing technique and we implement our approach based on it. Our approach is termed semi-supervised nearest neighbor editing (SSNNE). Our empirical evaluation using 12 UCI datasets shows that SSNNE outperforms KNN and Wilson editing in terms of generalization ability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Dahu:2008:ijcnn,
author = "Wang Dahu and Yang Haizhu and Yu Fashan and Wang Xudong",
title = "Research on A Novel One-way Trap-door Map Based on Improved Hyperbolic Function",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0412.pdf},
url = {},
size = {},
abstract = {In this paper, the properties of hyperbolic function are analysized at first; then a key exchange algorithm is proposed, which is based on improved hyperbolic function in combination with module computation.Moreover in comparison with the correspondent methods such as RSA and EIGamal etc., our algorithm is proven more secure and practical. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhao2:2008:ijcnn,
author = "Guopeng Zhao and Zhiqi Shen and Chunyan Miao and Robert Gay",
title = "Enhanced Extreme Learning Machine with Stacked Generalization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0414.pdf},
url = {},
size = {},
abstract = {This paper first reviews Extreme Learning Machine (ELM) in light of Cover's theorem and interpolation for a comparative study with Radial-Basis Function (RBF) networks. To improve generalization performance, a novel method of combining a set of single ELM networks using stacked generalization is proposed. Comparisons and experiment results show that the proposed stacking ELM outperforms a single ELM network for both regression and classification problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen8:2008:ijcnn,
author = "Omix Yu-Chian Chen and Guan-Wen Chen and WinstonYu-Chen Chen ",
title = "A Novel Strategy for the Structure-Based Drug Design of Heat Shock Protein 90 Inhibitors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0415.pdf},
url = {},
size = {},
abstract = {Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r2) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. These candidates might guide drug design for novel HSP90 inhibitors in the future. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fan:2008:ijcnn,
author = "Yu-Neng Fan and Chun-Che Huang and Ching-Chin Chern",
title = "Rule Induction Based on an Incremental Rough Set",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0417.pdf},
url = {},
size = {},
abstract = {The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous Rule Extraction Algorithm is proposed to resolve the aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hu:2008:ijcnn,
author = "Jinchun Hu and Badong Chen and Fuchun Sun and Zengqi Sun",
title = "Adaptive Filtering for Desired Error Distribution Under Minimum Information Divergence Criterion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0420.pdf},
url = {},
size = {},
abstract = {Conventional cost functions of adaptive filtering are usually related to the error's dispersion, such as error's moments or error's entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the error's probability density function (PDF) is shaped into a desired one. As PDFs contain all the probabilistic information, the proposed method can be used to achieve the desired error variance or error entropy, and is expected to be useful in the complex signal processing and learning systems. In our approach, the information divergence between the actual errors and the desired errors is used as the cost function. By kernel density estimation, we derive the associated stochastic gradient algorithm for the finite impulse response (FIR) filter. Simulation results emphasize the effectiveness of this new algorithm in adaptive system training. },
keywords = {Adaptive filtering, Information divergence, stochastic gradient algorithm, Kernel density estimation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Santana:2008:ijcnn,
author = "Laura E. A. Santana and Alberto Signoretti",
title = "An Analysis of Data Distribution Methods in Classifier Combination Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0423.pdf},
url = {},
size = {},
abstract = {In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this paper, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, five different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yin:2008:ijcnn,
author = "Hong Li Yin and Yong Ming Wang and Nan Feng Xiao and Yan Rong Jiang",
title = "Real-Time Remote Manipulation and Monitoring Architecture of an Industry Robot",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0424.pdf},
url = {},
size = {},
abstract = {Remote control and monitoring are very necessary in decentralized manufacturing environments. This is evidenced by today's distributed shop floors where agility and responsiveness are required to maintain high productivity and flexibility. However, there exists a lack of an effective system architecture that integrates remote condition monitoring and control of automated equipment; that give much consideration about the Internet data transfer time delay. This paper presented an Internet-based and sensor-driven architecture, which can guarantee the non-distortion-transfer of control information and reduce the action time difference between local simulated virtual robot and remote real robot, couple the remote monitoring and control together. For demonstrate and validate the architectural design, a 3 DOF industry robot remote operation and monitoring system has been developed. Experimental results are encouraging and demonstrate a promising application in advanced intelligent manufacturing environment. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Reznik:2008:ijcnn,
author = "Leon Reznik and Gregory Von Pless",
title = "Neural Networks for Cognitive Sensor Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0425.pdf},
url = {},
size = {},
abstract = {The paper puts forward a concept of cognitive sensor networks and investigates a feasibility of artificial neural networks application for its realization. It describes a design of novel hierarchical configurations imitating the structural topology of brain-like architectures. They are composed from artificial neural networks distributed over network platforms with limited resources. The paper examines a cognition idea based on its implementation through the signal change detection. The novel multilevel neural networks architectures are designed and tested in sensor networks built from Crossbow Inc. sensor kits. The results are compared against conventional multilayer perceptron structures in terms of both functional efficiency and resource consumption. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen9:2008:ijcnn,
author = "Omix Yu-Chian Chen ",
title = "Pharmacoinformatics Approach to the Discovery of Novel Selective COX-2 Inhibitors by in silico Virtual Screening",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0426.pdf},
url = {},
size = {},
abstract = {Various potent anti-cancer compounds, defined as group A, B, D, and YC-1 derivatives, were recruited for the simulation trails of selective inhibition to human cyclooxygenase-2 (COX-2). From our modeling, Leu530 and Ile522 would lead to the COX-1 binding site with a tunnel-like configuration. Compounds of group B would be suitable in the lobby of COX-1. In contract, the larger compounds, group A, D, and YC-1 derivatives were more potential against COX-2. As more the compounds bound in the similar pose indicates more the possible docking poses could happen, and thus generated the consensus pose. The anthraquinonyl group could be more suitable near the Tyr371 and Trp373 of COX-2, and the added hydroxyl group that interacted with Arg106/Tyr341 gate led the ligand more stable. In aspect of group D, the fused hydroxyl and aldehydyl group on one candidate attempted to interact with the gate which induced the whole construction more stable. Besides, the hydrophobic groups, fusing on some candidates and bounding between Ser516 and Tyr371 could stabilize the whole conformations in active site. We found the H-bond interactions between the gate of active site and the hydrophobic region among Ser516 and Tyr371 were important for the bound ligands in COX-2 active site. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nishida:2008:ijcnn,
author = "Kenji Nishida and Takio Kurita",
title = "Boosting with Cross-Validation Based Feature Selection for Pedestrian Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0427.pdf},
url = {},
size = {},
abstract = {An example-based classification algorithm to improve generalization performance for detecting objects in images is presented. The classifier integrates component-based classifiers according to the AdaBoost algorithm. A probability estimate by a kernel-SVM is used for the outputs of base learners, which are independently trained for local features. The base learners are determined by selecting the optimal local feature according to sample weights determined by the boosting algorithm with cross-validation. Our method was applied to the MIT CBCL pedestrian image database, and 54 sub-regions were extracted from each image as local features. The experimental results showed a good classification ratio for unlearned samples. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang3:2008:ijcnn,
author = "Kou-Yuan Huang and Ying-Liang Chou",
title = "Simulated Annealing for Hierarchical Pattern Detection and Seismic Applications",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0428.pdf},
url = {},
size = {},
abstract = {A Hierarchical system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures are type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Also we use the minimum error to determine the number of patterns. The proposed simulated annealing parameter detection system can search a set of parameter vectors for the global minimal error. In the experiments, using the hierarchical system, the result of the detection of a large number of simulated image patterns is better than that of using the synchronous system. In the seismic experiments, both of two systems can well detect line of direct wave and hyperbola of reflection wave in the simulated one-shot seismogram and the real seismic data, but the hierarchical system can converge faster. The results of seismic pattern detection can improve seismic interpretation and further seismic data processing. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Senoussi:2008:ijcnn,
author = "H. Senoussi and Chebel-Morello",
title = "A New Contextual Based Feature Selection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0429.pdf},
url = {},
size = {},
abstract = {The pre processing phase is essential in Knowledge Data Discovery process. We study too particularly the data filtering in supervised context, and more precisely the feature selection. Our objective is to permit a better use of the data set. Most of filtering algorithm use myopic measures, and give bad results in the case of the features correlated part by part. Consequently in the first time, we build two new contextual criteria. In the second part we introduce those criteria in an algorithm similar to the greedy algorithm. The algorithm is tested on a set of benchmarks and the results were compared with five reference algorithms: Relief, CFS, Wrapper (C4.5), consistancySubsetEval and GainRatio. Our experiments have shown its ability to detect the semi-correlated features. We conduct extensive experiments by using our algorithm like pre processing data for decision tree, nearest neighbours and Naïve Bays classifiers. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nieto:2008:ijcnn,
author = "Isidro B. Nieto and Jose Refugio Vallejo",
title = "A Decision Boundary Hyperplane for the Vector Space of Conics Using a Polinomial Kernel in m-Euclidean Space",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0430.pdf},
url = {},
size = {},
abstract = {The concept of linear perceptron or spherical perceptron in confomal geometry is extended to the more general conic perceptron, namely the elliptical perceptron. By means of the d-uple embedding a polynomial kernel of degree d is used, which is widely known in SVM's for neural networks. By associating the Clifford algebra to the Vector space of conics the conic separator is introduced, generalizing the notion of separator to a decision boundary hyperconic; which is independent of the dimension of the input space. Experimental results are shown in 2-dimensional Euclidean space where we separate data that are naturally separated by some typical plane conic separators by this polynomial kernel. This is more general in the sense that it is independent of the dimension of the input data and hence we can speak of the hyperconic elliptic perceptron by using a higher degree polynomial kernel. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheng3:2008:ijcnn,
author = "Jianlin Cheng and Zheng Wang and Gianluca Pollastri",
title = "A Neural Network Approach to Ordinal Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0431.pdf},
url = {},
size = {},
abstract = {Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data mining tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics. The neural network software is available at: http://www.cs.missouri.edu/ ~ chengji/cheng_software.html. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Peres:2008:ijcnn,
author = "Sarajane M. Peres and Marcio L. de A. Netto",
title = "The Meaningful Fractal Fuzzy Dimension Applied to the Design of Self Organizing Maps",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0432.pdf},
url = {},
size = {},
abstract = {This paper presents the principal results of a detailed study about the use of the Meaningful Fractal Fuzzy Dimension measure in the problem in determining adequately the topological dimension of output space of a Self-Organizing Map. This fractal measure is conceived by combining the Fractals Theory and Fuzzy Approximate Reasoning. In this work this measure was applied on the dataset in order to obtain a priori knowledge, which is used to support the decision making about the SOM output space design. Several maps were designed with this approach and their evaluations are discussed here. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qiu2:2008:ijcnn,
author = "Qinru Qiu and Daniel Burns and Michael Moore",
title = "Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0433.pdf},
url = {},
size = {},
abstract = {Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software and hardware optimization. On the software implementation side, appropriate data structures can improve the performance of the software by more than 5,000X. On the hardware implementation side, the cogent confabulation algorithm is an ideal candidate for parallel processing and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost tradeoffs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve good cost performance tradeoffs. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tepvorachai:2008:ijcnn,
author = "Gorn Tepvorachai and Chris Papachristou",
title = "Multi-Label Imbalanced Data Enrichment Process in Neural Net Classifier Training",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0435.pdf},
url = {},
size = {},
abstract = {Semantic scene classification, robotic state recognition, and many other real-world applications involve multilabel classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm controller show that our method has better generalization performance than traditional neural net training in solving the multi-label and imbalanced data problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bergamini:2008:ijcnn,
author = "Cheila Bergamini and Luiz S. Oliveira and Alessandro L. Koerich and Robert Sabourin",
title = "Fusion of Biometric Systems using One-Class Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0436.pdf},
url = {},
size = {},
abstract = {One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but considering the one-class classification approach. We show that one-class classification could be considered as an alternative for biometric fusion specially when the data is highly unbalanced or data from a single class is available. The results for one-class classification reported in this paper compares to the standard two-class SVM and surpasses all the conventional classifier combination rules tested. },
keywords = { One-class classification, multimodal biometric systems},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gharavol:2008:ijcnn,
author = "Ebrahim A. Gharavol and Ooi Ban Leong",
title = "Blind Source Separation and Bearing Estimation Using Fourier- and Wavelet-Based Spectrally Condensed Data and Artificial Neural Networks for Indoor Environments",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0442.pdf},
url = {},
size = {},
abstract = {A new method for blind separation and bearing estimation of wavefronts in a smart antenna scheme, which is based on the usage of artificial neural networks (ANN) is presented here. Because of ``the curse of dimensionality,'' especially in the cases having many antenna elements, in uniform linear, circular or planar arrays, it is important to find a method which makes it feasible to use the ANNs. The proposed method, do not walk along the road of well-known method of correlation-coefficient training. In contrast this method uses the truncated version of their spectral representations. The Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) are employed to provide the spectral representations. The simulation scenario is set up to demonstrate that the results is applicable to realistic cases such as urban, non-line of sight, and indoor environments. For the sake of this purpose, coherent signals are employed in simulations. In this case, most conventional methods are not applicable, because they are built on some statistical assumptions which implies that the received signals by array must be independent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(He:2008:ijcnn,
author = "Haibo He and Yang Bai and Edwardo A. Garcia and Shutao Li",
title = "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0444.pdf},
url = {},
size = {},
abstract = {This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mendoza:2008:ijcnn,
author = "Olivia Mendoza and Patricia Melín and Guillermo Licea",
title = "Estimating Module Relevance with Sugeno Integration of Modular Neural Networks using Interval Type-2 Fuzzy Logic",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0445.pdf},
url = {},
size = {},
abstract = {In this paper a Fuzzy Logic approach to determine the relevance of each module in Modular Neural Networks for images recognition is presented. The tests were made with Type-1 and Interval Type-2 Fuzzy Inference Systems, to compare the performance of the proposed approach. In both cases the fusion operator for the modules is the Sugeno Integral, and the estimated parameters are the fuzzy densities. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lu3:2008:ijcnn,
author = "Bing Lu and Alireza Dibazar and Theodore W. Berger ",
title = "Nonlinear Hebbian Learning for Noise-Independent
Vehicle Sound Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0446.pdf},
url = {},
size = {},
abstract = {In this paper we propose using a new approach, a nonlinear Hebbian learning, to implement acoustic signature recognition of running vehicles. The proposed learning rule processes both time and frequency components of input data. The spectral analysis is realized using auditory gammatone filterbanks. The gammatone-filtered feature vectors are then assembled over multiple temporal frames to establish a highdimensional spectro-temporal representation (STR). With the exact acoustic signature of running vehicles being unknown, a nonlinear Hebbian learning (NHL) rule is employed to extract representative independent features from the spectro-temporal ones and to reduce the dimensionality of the feature space. During learning, synaptic weights between input and output neurons are adaptively learned. Motivated by neurobiological synaptic transmission in the brain, one specific nonlinear activation function, which can represent multiple independent neural signaling pathways, is proposed to process nonlinear Hebbian learning. It is shown that this function satisfies the requirements of the activation function in nonlinear neural learning, and that its derivative matches the implicit distribution of vehicle sounds, thus leading to a statistically optimal learning. Simulation results show that both STR and NHL can accurately extract critical features from original input data. The proposed model achieves better performance under noisy environments than its counterparts. For additive white Gaussian noise and common coloured noise, the proposed model demonstrates excellent robustness. It can decrease the error rate to 3percent with improvement 21 ~ 34percent at signal-to-noise ratio (SNR) = 0 dB, and can function efficiently with error rate 7 ~ 8percent at low SNR6dB when its counterparts cannot work properly at this situation. To summarize, this study not only provides an efficient way to capture important features from high-dimensional input signals but also offers robustness against severe background noise. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kumar:2008:ijcnn,
author = "Swagat Kumar and Laxmidhar Behera",
title = "Implementation of a Neural Network Based Visual Motor Control Algorithm for a 7 DOF Redundant Manipulator",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0449.pdf},
url = {},
size = {},
abstract = {This paper deals with visual-motor coordination of a 7 dof robot manipulator for pick and place applications. Three issues are dealt with in this paper — finding a feasible inverse kinematic solution without using any orientation information, resolving redundancy at position level and finally maintaining the fidelity of information during clustering process thereby increasing accuracy of inverse kinematic solution. A 3-dimensional KSOM lattice is used to locally linearize the inverse kinematic relationship. The joint angle vector is divided into two groups and their effect on end-effector position is decoupled using a concept called function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. However, this method yields a unique inverse kinematic solution for a given target point. A concept called sub-clustering in configuration space is suggested to preserve redundancy during learning process and redundancy is resolved at position level using several criteria. Even though the training is carried out off-line, the trained network is used online to compute the required joint angle vector in only one step. The accuracy attained is better than the current state of art. The experiment is implemented in real-time and the results are found to corroborate theoretical findings. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Stubberud:2008:ijcnn,
author = "Stephen C. Stubberud and Kathleen A. Kramer",
title = "System Identification Using the Neural-Extended Kalman
Filter for State-Estimation and Controller Modification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0455.pdf},
url = {},
size = {},
abstract = {The neural extended Kalman filter (NEKF) is an adaptive state estimation technique that can be used in target tracking and directly in a feedback loop. It improves state estimates by learning the difference between the a priori model and the actual system dynamics. The neural network training occurs while the system is in operation. Often, however, due to stability concerns, such an adaptive component in the feedback loop is not considered desirable by the designer of a control system. Instead, the tuning of parameters is considered to be more acceptable. The ability of the NEKF to learn dynamics in an open-loop implementation, such as with target tracking and intercept prediction, can be used to identify mismodeled dynamics external to the closed-loop system. The improved nonlinear system model can then be used at given intervals to adapt the state estimator and the state feedback gains in the control law, providing better performance based on the actual system dynamics. This variation to neural extended Kalman filter control operations is introduced in this paper using applications to the nonlinear version of the standard cartpendulum system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kimura:2008:ijcnn,
author = "Masahiro Kimura and Kazumasa Yamakawa and Kazumi Saito and Hiroshi Motoda",
title = "Community Analysis of Influential Nodes for Information
Diffusion on a Social Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0456.pdf},
url = {},
size = {},
abstract = {We consider the problem of finding influential nodes for information diffusion on a social network under the independent cascade model. It is known that the greedy algorithm can give a good approximate solution for the problem. Aiming to obtain efficient methods for finding better approximate solutions, we explore what structual feature of the underlying network is relevant to the greedy solution that is the approximate solution by the greedy algorithm. We focus on the SR-community structure, and analyze the greedy solution in terms of the SR-community structure. Using real large social networks, we experimentally demonstrate that the SRcommunity structure can be more strongly correlated with the greedy solution than the community structure introduced by Newman and Leicht. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gu:2008:ijcnn,
author = "Bin Gu and Jiandong Wang and Haiyan Chen ",
title = "On-line Off-line Ranking Support Vector Machine and Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0459.pdf},
url = {},
size = {},
abstract = {Ranking Support Vector Machine (RSVM) learning is equivalent to solving a convex quadratic programming problem. Currently there exists some difficulties for exact online ranking learning. This paper presents an exact and effective method that can solve the on-line ranking learning problem and shows the feasibility and finite convergence of the algorithm from the perspective of theoretical analysis. Additionally, this paper extends this method for on-line learning to off-line ranking learning and offers another algorithm for solving largescale RSVM},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Makki:2008:ijcnn,
author = "B. Makki and M. Noori Hosseini and S. A. Seyyedsalehi",
title = "Unsupervised Extraction of Meaningful Nonlinear Principal Components Applied for Voice Conversion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0461.pdf},
url = {},
size = {},
abstract = {Nonlinear Principal Component Analysis (NLPCA) is one of the most progressive computational tools developed during the last two decades. However, in spite of its proper performance in feature extraction and dimension reduction, it is considered as a blind processor which can not extract physical or meaningful factors from dataset. This paper presents a new distributed model of autoassociative neural network which increases meaningfulness degree of the extracted parameters. The model is implemented to perform Voice Conversion (VC) and, as it will be seen through comparisons, results in proper conversion quality. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Minku:2008:ijcnn,
author = "Fernanda L. Minku and Xin Yao",
title = "On-line Bagging Negative Correlation Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0463.pdf},
url = {},
size = {},
abstract = {Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the same sequence of training data is sent to all the ensemble members. In this way, the choice of the base models to be used is limited and the use of more adequate neural network models for the problem to be solved may be not possible. This paper proposes a new method to perform on-line learning based on NCL and On-line Bagging. The method directly encourages diversity, as NCL, but sends a different sequence of training data to each one of the base models in an on-line bagging way. So, it allows the use of deterministic base models such as Evolving Fuzzy Neural Networks (EFuNNs), which are specifically designed to perform on-line learning. Experiments show that on-line bagging NCL using EFuNNs have better accuracy than NCL applied to online learning using on-line Multi-Layer Perceptrons (MLPs) in 4 out of 5 classification databases. Besides, on-line bagging NCL using EFuNNs manage to attain similar accuracy to NCL using off-line MLPs. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Korsrilabutr:2008:ijcnn,
author = "Teesid Korsrilabutr and Boonserm Kijsirikul",
title = "Pseudometrics for Time Series Classification by Nearest Neighbor",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0464.pdf},
url = {},
size = {},
abstract = {Despite the success of its applications in many areas, the Dynamic Time Warping (DTW) distance does not satisfy the triangle inequality (subadditivity). Once we have a subadditive distance measure for time series, the measure will have at least one significant advantage over DTW; one can directly plug such distance measure into systems which exploit the subadditivity to perform faster similarity search techniques. We propose two frameworks for designing subadditive distance measures and a few examples of distance measures resulting from the frameworks. One framework is more general than the other and can be used to tailor distances from the other framework to gain better classification performance. Experimental results of nearest neighbor classification showed that the designed distance measures are practical for time series classification. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Calster:2008:ijcnn,
author = "Ben Van Calster and Vanya Van Belle and George Condous and Tom Bourne",
title = "Multi-class AUC metrics and weighted alternatives",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0466.pdf},
url = {},
size = {},
abstract = {The area under the receiver operating characteristic curve (AUC) is a useful and widely used measure to evaluate the performance of binary and multi-class classification models. However, it does not take into account the exact numerical output of the models, but rather looks at how the output ranks the cases. AUC metrics that incorporate the exact numerical output have been developed for binary classification. In this paper, we try to extend such weighted metrics to the multiclass case. Several metrics are suggested. Using real world data, we investigate intercorrelations between these metrics and demonstrate their use. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Duan:2008:ijcnn,
author = "Haibin Duan and Senqi Liu and Xiujuan Lei",
title = "Air Robot Path Planning Based on Intelligent Water Drops Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0469.pdf},
url = {},
size = {},
abstract = {Path planning of air robot is a complicated global optimum problem. Intelligent Water Drops (IWD) algorithm is newly presented under the inspiration of the dynamic of river systems and the actions that water drops do in the rivers, and it is easy to combine with other methods in optimization. In this paper, we propose an improved IWD optimization algorithm for solving the air robot path planning problems in various environments. The water drops can act as an agent in searching the optimal path. The detailed realization procedure for this novel approach is also presented. Series experimental comparison results show the proposed IWD optimization algorithm is more effective and feasible in the air robot path planning than the basic IWD model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tao2:2008:ijcnn,
author = "Dacheng Tao and Jimeng Sun and Jialie Shen",
title = "Bayesian Tensor Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0470.pdf},
url = {},
size = {},
abstract = {Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and tensors in conventional statistical tasks, e.g., automatic model selection, this paper proposes a decoupled probabilistic algorithm, named Bayesian tensor analysis (BTA). BTA automatically selects a suitable model for tensor data, as demonstrated by empirical studies. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shrestha:2008:ijcnn,
author = "Durga Lal Shrestha and Dimitri P. Solomatine",
title = "Comparing Machine Learning Methods in Estimation of Model Uncertainty",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0471.pdf},
url = {},
size = {},
abstract = {The paper presents a generalization of the framework for assessment of predictive models uncertainty using machine learning techniques. Historical model errors which are mismatch between observed and predicted values are assumed to be indicators of total model uncertainty; it is measured in the form of prediction intervals, and comprises all sources of uncertainty including model structure, model parameters, input and output data. Several machine learning methods are compared. The approach is tested on a conceptual hydrological model set up to predict stream flows of the Brue catchment in the United Kingdom. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu6:2008:ijcnn,
author = "Derong Liu and Ning Jin",
title = "ε-Adaptive Dynamic Programming for Discrete-Time Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0474.pdf},
url = {},
size = {},
abstract = {Dynamic programming for discrete-time systems is difficult due to the "curse of dimensionality": one has to find a series of control actions that must be taken in sequence. This sequence will lead to the optimal performance cost, but the total cost of those actions will be unknown until the end of that sequence. In this paper, we present our work on dynamic programming for discrete-time system, which is referred as ε-Adaptive Dynamic Programming. A single controller, ε-optimal controller u(ε)*, which is determined from an ε-optimal cost J(ε)*, is given to approximate the optimal controller. The ε-optimal controller u(ε)* can always control the state to approach to the equilibrium state, while the performance cost is close to the biggest lower bound of all performance costs within an error according to ε. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Saha:2008:ijcnn,
author = "Sriparna Saha and Sanghamitra Bandyopadhyay and Chingtham Tejbanta Singh",
title = "A New Line Symmetry Distance Based Pattern Classifier",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0475.pdf},
url = {},
size = {},
abstract = {In this paper, a new line symmetry based classifier (LSC) is proposed to deal with pattern classification problems. In order to measure total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also proposed in this paper. The proposed line symmetry based classifier (LSC) uses this new definition of line symmetry distance for classifying an unknown test sample. LSC assigns an unknown test sample pattern to that class with respect to whose major axis it is most symmetric. The mean of all the training patterns belonging to that particular class is taken as the prototype of that class. Thus training constitutes of computing only the class prototypes and the major axes of those classes. Kd-tree based nearest neighbor search is used for reducing complexity of line symmetry distance computation. The performance of LSC is demonstrated in classifying twelve artificial and real-life data sets of varying complexities. Experimental results show that LSC achieves, in general, higher classification accuracy compared to k-NN classifier. Results indicate that the proposed novel line symmetry based classifier is well-suited for classifying data sets having symmetrical classes, irrespective of any convexity, overlap and size. Statistical analysis, ANOVA is also performed to compare the performance of these classifications techniques. },
keywords = { Pattern Classification, Kd-tree, Symmetry based distance, Nearest Neighbor Rule, Line Symmetry },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Miao:2008:ijcnn,
author = "Qingliang Miao and Qiudan Li",
title = "An opinion search system for consumer products",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0476.pdf},
url = {},
size = {},
abstract = {With the rapid progress of e-commerce, many people like purchasing product on the e-commerce website, and giving their personal reviews to the product they purchased, so the number of customer reviews grows rapidly. Generally, a potential customer will browse product reviews before they purchase the product. However, retrieving opinions relevant to customer's desire still remains challenging. To provide efficient opinion information for customers, we propose an opinion search system for consumer products, which uses data mining and information retrieval technology. A ranking mechanism taking temporal dimension into account and a method for results visualization are developed in the system. Experimental results on a real-world data set show the system is feasible and effective. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang5:2008:ijcnn,
author = "Jun Zhang and Ning Li and Y. F. Li ",
title = "The Detection of Multiple Moving Objects Using Fast Level Set Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0477.pdf},
url = {},
size = {},
abstract = {A novel method for the detection of multiple moving objects is proposed in this paper. In order to get the detailed information of the objects, fast level set method which is only based on the evolution of single link list is mainly used to detect the boundaries of moving objects. The whole process consists of two main procedures: the coarse detection and the fine localization. During the coarse detection procedure, the velocity function is defined according to the modified Otsu method which is more effective to eliminate the split phenomena of the whole motion area and get the consecutive boundaries. As to the fine localization, improved region competition is applied to obtain the smooth and exact contours. The proposed method has been tested on several different video sequences, and the efficiency of the method has been verified. },
keywords = {: Object detection, object tracking, level set method, region competition, moving objects, video sequences.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu7:2008:ijcnn,
author = "Wenju Liu and Yun Tang and Shouye Peng",
title = "Fast and Robust Stochastic Segment Model for Mandarin Digital
String Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0480.pdf},
url = {},
size = {},
abstract = {Based on the analysis and comparisons of complexity between stochastic segment model (SSM) and hidden Markov model (HMM) in this paper, we presented a fast and robust SSM, which yields a 94.75percent speaker-independent performance on Mandarin digit string recognition. This result is better than HMM based system at the same level of computational complexity and just only a little slower than HMM in the running time. We also studied a region based discriminative method, which achieves 18.0percent error rate reduction for substitution error and 95.08percent accuracy for Mandarin digit string recognition. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jiang4:2008:ijcnn,
author = "Aiwen Jiang and Chunheng Wang and Yuanping Zhu",
title = "Calibrated Rank-SVM for Multi-Label Image Categorization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0482.pdf},
url = {},
size = {},
abstract = {In the area of multi-label image categorization, there are two important issues: label classification and label ranking. The former refers to whether a label is relevant or not, and the latter refers to what extent a label is relevant to an image. However, few existing papers have considered them in a holistic way. In this paper we will suggest a concrete improved method, named calibrated RankSVM, to bridge the gap between multi-label classification and label ranking. Through incorporating a virtual label as a calibrated scale [1], the threshold selection stage is embedded into ranking learning stage. This holistic way is essentially different from conventional rank methods, making our proposed method more suitable for multi-label classification task. The experiments on image have demonstrated that our algorithm has better multi-label classification performances than conventional ranksvm while preserving its good ranking characteristics. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Angelov:2008:ijcnn,
author = "Plamen Angelov and Ramin Ramezani and Xiaowei Zhou ",
title = "Autonomous Novelty Detection and Object Tracking in Video Streams Using Evolving Clustering and Takagi-Sugeno Type Neuro-Fuzzy System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0483.pdf},
url = {},
size = {},
abstract = {Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on colour intensity information is often preferred than the motion cues due to being more robust. The technique presented in this paper can also be used in conjunction with infra-red cameras, 3D lasers which result in a grey scale image. Novelty detection and tracking are two of the key elements of such systems. Most of the currently reported techniques are characterized by high computational, memory storage costs and are not autonomous because they usually require a human operator in the loop. This paper presents new approaches to both the problem of novelty detection and object tracking in video streams. These approaches are rooted in the recursive techniques that are computationally efficient and therefore potentially applicable in real-time. A novel approach for recursive density estimation (RDE) using a Cauchy type of kernel (as opposed to the usually used Gaussian one) is proposed for visual novelty detection and the use of the recently introduced evolving Takagi-Sugeno (eTS) neuro-fuzzy system for tracking the object detected by the RDE approach is proposed as opposed to the usually used Kalman filter (KF). In fact, eTS can be seen as a fuzzily weighted mixture of KF. The proposed technique is significantly faster than the well known kernel density estimation (KDE) approach for background subtraction for novelty detection and is more precise than the usually used KF. Additionally the overall approach removes the need of manually selecting the object to be tracked which makes possible a fully autonomous system for novelty detection and tracking to be developed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bamford:2008:ijcnn,
author = "Simeon A. Bamford and Alan F. Murray and David J. Willshaw",
title = "Large Developing Axonal Arbors Using a Distributed and
Locally-Reprogrammable Address-Event Receiver",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0484.pdf},
url = {},
size = {},
abstract = {We have designed a distributed and locally reprogrammable address event receiver. Incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change input address, allowing neurons to implement a biologically realistic learning rule locally, with both synapse formation and elimination. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Julia:2008:ijcnn,
author = "Fatema N. Julia and Khan M. Iftekharuddin ",
title = "Dialog Act Classification using Acoustic and Discourse Information of MapTask Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0485.pdf},
url = {},
size = {},
abstract = {In this work, we analyze both acoustic and discourse information for Dialog Act (DA) classification of HCRC MapTask dataset. We extract several different acoustic features and exploit these features in a Hidden Markov Model (HMM) to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features manyfold. To classify discourse information, we exploit two classifiers such as a HMM and a Support Vector Machine (SVM) respectively. We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify twelve different DAs in HCRC MapTask data. We obtain accuracy of rate 65.2percent (58.06percent with cross validation) and 55.4percent (51.08percent with cross validation) DA classification using acoustic and discourse information respectively. Furthermore, we obtain combined accuracy of 68.6percent (61.02percent with cross validation) for DA classification. These accuracy rates of DA classification are comparable to previously reported results for the same HCRC MapTask dataset. In terms of average Precision and Recall, we obtain accuracy of 74.89percent and 69.83percent (without cross validation) respectively. Therefore, we obtain much better precision and recall rate for most of the classified DAs when compared to existing works on the same dataset },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Souto:2008:ijcnn,
author = "Marcilio C. P. de Souto and Rodrigo G. F. Soares and Alixandre Santana and Anne M. P. Canuto",
title = "Empirical Comparison of Dynamic Classifier Selection Methods Based on Diversity and Accuracy for Building Ensembles",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0487.pdf},
url = {},
size = {},
abstract = {In the context of Ensembles or Multi-Classifier Systems, the choice of the ensemble members is a very complex task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a great deal of research to find effective classifier member selection methods. In this paper, we propose a selection criterion based on both the accuracy and diversity of the classifiers in the initial pool. Also, instead of using a static selection method, we use a Dynamic Classifier Selection (DSC) procedure. In this case, the member classifiers to form the ensemble are chosen at the test (use) phase. That is, different testing patterns can be classified by different ensemble configurations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tsaih:2008:ijcnn,
author = "Rua-Huan Tsaih and Yat-wah Wan and Shin-Ying Huang",
title = "The Rule-Extraction Through the Preimage Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0489.pdf},
url = {},
size = {},
abstract = {This study reveals the properties of the input/output relationship for a real-valued single-hidden layer feed-forward neural network (SLFN) with the tanh activation function on all hidden-layer nodes and the linear activation function on output node. Specifically, the rule-extraction of the SLFN is done through mathematically analyzing its preimage, which is the set of input values for a given output value. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Al-Mamory:2008:ijcnn,
author = "Safaa O. Al-Mamory and Zhang Hongli and Ayad R. Abbas",
title = "Modeling Network Attacks for Scenario Construction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0491.pdf},
url = {},
size = {},
abstract = {The Intrusion detection system (IDS) is a security technology that attempts to identify network intrusions. Defending against multistep intrusions which prepare for each other is a challenging task. In this paper, the Context-Free Grammar (CFG) was used to describe the multistep attacks using alerts classes. Based on the CFGs, the modified LR parser was employed to generate the parse trees of the scenarios presented in the alerts. Instead of searching all the received alerts for those that prepare for a new alert, we only search for the latest alert's type of each scenario. Consequently, the proposed system has an attractive time complexity. The experiments were performed on two different sets of network traffic traces, using different open-source and commercial IDSs. The detected scenarios are represented by Correlation Graphs (CGs). The experimental results show that the CFG can describe multistep attacks explicitly and the modified LR parser, based on the CFG, can construct scenarios successfully. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lopez:2008:ijcnn,
author = "Miguel Lopez and Patricia Melin",
title = "Response Integration in Ensemble Neural Networks Using Interval
Type-2 Fuzzy Logic",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0493.pdf},
url = {},
size = {},
abstract = {This paper describes a new approach for response integration in Ensemble Neural Networks using interval type-2 Fuzzy logic. When using ensemble neural networks it is important to choose a good method of Response Integration to obtain a better identification in pattern recognition. In this paper a comparative analysis between interval type-2 fuzzy logic, type-1 fuzzy logic and the Sugeno Integral, as response integration methods, in ensemble neural networks is presented. Based on Simulation results Interval type-2 fuzzy logic is shown to be a superior method for response integration. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang6:2008:ijcnn,
author = "Wenfeng Zhang and Zhongke Shi and Zhiyong Luo",
title = "Prediction of Urban Passenger Transport Based-On Wavelet SVM with Quantum-Inspired Evolutionary Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0496.pdf},
url = {},
size = {},
abstract = {Based on least squares wavelet support vector machines (LS-WSVM) with quantum-inspired evolutionary algorithm (QEA), the prediction model of urban passenger transport is proposed, that can provide the theoretical foundation of forecasting passenger volume of urban transport accurately. The prediction model of urban passenger transport is established by using LS-WSVM, whose regularization parameter and kernel parameter are adjusted using quantum-inspired evolutionary algorithm. QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of LS-WSVM can be adjusted using quantuminspired evolutionary optimization. Combining with the data of the urban volume of passenger transport of Xi'an over years, the prediction model of urban passenger transport is validated, the simulation results indicate that the prediction model is effective, and based on LS-WSVM has more improvement than LS-SVM with Gaussian kernel in predicting precision, and then the improved LS-WSVM with QEA is efficient than with crossvalidation method for tuning parameters. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Goh:2008:ijcnn,
author = "Hanlin Goh and Joo-Hwee Lim and Chai Quek",
title = "Learning Associations of Conjuncted Fuzzy Sets for Data Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0497.pdf},
url = {},
size = {},
abstract = {Fuzzy Associative Conjuncted Maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These associations are learnt through a learning process consisting of three consecutive phases. First, an unsupervised phase initializes based on information density the fuzzy membership functions that partition each feature map. Next, a supervised Hebbian learning phase encodes synaptic weights of the input-output associations. Finally, a supervised error reduction phase fine-tunes the fine-tunes the network and discovers the varying influence of an input dimension across output feature space. FASCOM was benchmarked against other prominent architectures using data taken from three nonlinear data estimation tasks and a realworld road traffic density prediction problem. The promising results compiled show significant improvements over the stateof-the-art for all four data prediction tasks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Krasnopolsky:2008:ijcnn,
author = "Vladimir Krasnopolsky and Michael S. Fox-Rabinovitz and Alexei Belochitski",
title = "Using Neural Network Emulations of Model Physics in Numerical
Model Ensembles",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0498.pdf},
url = {},
size = {},
abstract = {In this paper the use of the neural network emulation technique, developed earlier by the authors, is investigated in application to ensembles of general circulation models used for the weather prediction and climate simulation. It is shown that the neural network emulation technique allows us: (1) to introduce fast versions of model physics (or components of model physics) that can speed up calculations of any type of ensemble up to 2-3 times; (2) to conveniently an naturally introduce perturbations in the model physics (or a component of model physics) and to develop a fast versions of perturbed model physics (or fast perturbed components of model physics), and (3) to make the computation time for the entire ensemble (in the case of short term perturbed physics ensemble introduced in this paper) comparable with the computation time that is needed for a single model run. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kim2:2008:ijcnn,
author = "Hyun-Soo Kim and Bryan G. Morris and Seung-Soo Han and Gary S. May",
title = "A Comparison of Genetic and Particle Swarm Optimization for Contact Formation in High-Performance Silicon Solar Cells",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0499.pdf},
url = {},
size = {},
abstract = {In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several input factors and solar cell efficiency. Subsequently, both genetic algorithms and particle swarm optimization are used to identify the optimal process conditions to maximize cell efficiency. The results of the two approaches are compared, and the optimized efficiency found via the particle swarm method was slightly larger than the value determined via genetic algorithms. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller standard deviations, implying greater consistency in recipe generation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Valdez:2008:ijcnn,
author = "Fevrier Valdez and Patricia Melin and Olivia Mendoza",
title = "A New Evolutionary Method with Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms: The Case of Neural Networks Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0500.pdf},
url = {},
size = {},
abstract = {We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network optimization. The new hybrid PSO+GA method is shown to be superior with respect to both the individual evolutionary methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(He2:2008:ijcnn,
author = "Fei He and Martin Brown and Hong Yue",
title = "Robust Experimental Design and Feature Selection in Signal
Transduction Pathway Modeling",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0503.pdf},
url = {},
size = {},
abstract = {Due to the general lack of experimental data for biochemical pathway model identification, cell-level time series experimental design is particularly important in current systems biology research. This paper investigates the problem of experimental design for signal transduction pathway modeling, and in particular, focuses on methods for parametric feature selection. An important problem is the estimation of parametric uncertainty which is a function of the true (but unknown) parameters. In this paper, two "robust" feature selection strategies are investigated. The first is a mini-max robust experimental design approach, the second is a sampled experimental design method inspired by the Morris global sensitivity analysis. The two approaches are analyzed and interpreted in terms of a generalized optimal experimental design criterion, and their performance has been compared via simulation on the IκB-NF-κB pathway feature selection problem. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang7:2008:ijcnn,
author = "Hui Wang and Chuandong Li and Yongguang Yu",
title = "An Estimate of Impulse Bounds in Delayed BAM Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0504.pdf},
url = {},
size = {},
abstract = {This paper further studies the exponential stability of delayed bidirectional associative memory neural networks and focuses on the impulse effect on the exponential stability property. It is shown that if the corresponding impulse-free DBAM is globally exponentially stable the impulsive analog will remain its stability property even if the measurements of the states are magnified to some extent at the impulse instants. Furthermore, the admissible upper bound of impulse is estimated in terms of exponential convergence degree of the corresponding impulse-free DBAM and the length of impulse interval. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo4:2008:ijcnn,
author = "Baosheng Guo and Ruoen Ren",
title = "Nonparametric Approach for Estimating Dynamics of Stock Index",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0505.pdf},
url = {},
size = {},
abstract = {Parametric and nonparametric methods are used in estimating stochastic diffusion process. Nonparametric method has its own advantages; this paper uses nonparametric method to estimate drift and diffusion term. Two nonparametric methods have been studied, which are kernel estimation and local linear estimation. Local linear estimation has been used in estimating dynamics of Shanghai Stock Exchange Composite Index. },
keywords = {: Stochastic diffusion process, kernel regression, local linear estimation, stock indices.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ferreira:2008:ijcnn,
author = "Rita Ferreira and Bernardete Ribeiro and Catarina Silva and Qingzhong Liu and Andrew H Sung",
title = "Building Resilient Classifiers for LSB Matching Steganography",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0506.pdf},
url = {},
size = {},
abstract = {One of the Internet's hallmark is the rapid spread of the use of information and communication technology. This has boosted methods for hiding stego information inside digital cover content images which is a concerning issue in information security. On the other hand, attack of steganographic schemes has leveraged methods for steganalysis which is a challenging problem. In this paper, first we look at the design of classifiers, such as, Support Vector Machines (SVM) and neural networks (RBF and MLP) which are able to detect the presence of Least Significant Bit (LSB) matching steganography of gray scale images. Second, by combining with feature ranking methods (SVM-Recursive Feature Elimination, Kruskal Wallis) and reduction techniques (PCA) pattern classification of stego is successfully achieved. It is of utmost importance to look at the large set of features extracted from images and find ranking methods able, namely, to exclude correlated and redundant features, avoid the curse of dimensionality or circumvent the need of the steganalyzer to be re-designed. Results show that desirable properties of robustness and resilience are attained by designing classifiers able to deal with redundancy and noise. Moreover, comparison of classifiers performance emphasizes the chosen model for the steganalyser. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Olier2:2008:ijcnn,
author = "Ivan Olier and Alfredo Vellido",
title = "On the Benefits for Model Regularization of a Variational
Formulation of GTM",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0508.pdf},
url = {},
size = {},
abstract = {Generative Topographic Mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this formulation, GTM is prone to data overfitting unless a regularization mechanism is included. The theoretical principles of Variational GTM, an approximate method that provides a full Bayesian treatment to a Gaussian Process (GP)-based variation of the GTM, were recently introduced as alternative way to control data overfitting. In this paper we assess in some detail the generalization capabilities of Variational GTM and compare them with those of alternative regularization approaches in terms of test log-likelihood, using several artificial and real datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jin:2008:ijcnn,
author = "Fengxiang Jin and Shifei Ding",
title = "An Improved PCA Algorithm Based on WIF",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0509.pdf},
url = {},
size = {},
abstract = {In this paper, we analyze the information feature of principal component analysis (PCA) deeply based on information entropy. According to idea of entropy function, a new weighted information functions (WIF) is proposed, and the information content of data matrix X is measured by it. Based on WIF, the information compression rate (ICR, RIC) and accumulated information compression rate (AICR, RAIC) are set up, by which the degree of information compression is measured. At last, an improved PCA algorithm (IPCA) based on WIF is constructed. Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of feature compression for pattern recognition, machine learning, data mining and so on. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Policastro:2008:ijcnn,
author = "Claudio A. Policastro and Giovana Zuliani and Renato R. da Silva",
title = "Hybrid Knowledge Representation Applied to the Learning of the
Shared Attention",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0510.pdf},
url = {},
size = {},
abstract = {Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They are able to recognize human beings and each other, and engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. However, a robotic architecture for sociable robots must have structures and mechanisms to allow social interaction, behavior control and learning from environment. In this article, a new hybrid knowledge representation is proposed and integrated to our robotic architecture inspired on Behavior Analysis. This new hybrid knowledge representation enables incremental learning and knowledge generalization by incorporating an ART2 Neural Network combined with a relational presentation of first order. The new representation has been evaluated in the context of the learning of the shared attention and the results obtained show that it is a very promising approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yan:2008:ijcnn,
author = "Weizhong Yan and Feng Xue",
title = "Jet Engine Gas Path Fault Diagnosis Using Dynamic Fusion of Multiple Classifiers",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0512.pdf},
url = {},
size = {},
abstract = {Jet engine gas path fault diagnosis is not only important in modern condition-based maintenance of aircraft engines, but also a challenging classification problem. Exploring more advanced classification techniques for achieving improved classification performance for gas path fault diagnosis, therefore, has been increasingly active in recent years in PHM community. To that end, in this paper, we apply a recently developed dynamic fusion scheme to gas path fault diagnosis. Through designing a real-world gas path fault diagnostic system, we demonstrate that dynamic fusion of multiple classifiers can be effective in improving classification performance of gas path diagnosis. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lee:2008:ijcnn,
author = "Hyekyoung Lee and Seungjin Choi",
title = "CUR+NMF for Learning Spectral Features from Large Data Matrix",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0515.pdf},
url = {},
size = {},
abstract = {Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data. It was successfully applied to learn spectral features from EEG data. However, the size of a data matrix grows, NMF suffers from 'out of memory' problem. In this paper we present a memory-reduced method where we downsize the data matrix using CUR decomposition before NMF is applied. Experimental results with two EEG data sets in BCI competition, confirm the useful behavior of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Syrris:2008:ijcnn,
author = "Vassilis Syrris and Vassilios Petridis",
title = "Classification Through Hierarchical Clustering and Dimensionality Reduction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0516.pdf},
url = {},
size = {},
abstract = {This work describes a two-mode clustering hierarchical model capable of dealing with high dimensional data spaces. The algorithm seeks a transformed subspace which can represent the initial data, simplify the problem and possibly lead to a better categorization level. We test the algorithm on two hard classification problems, the phoneme and the pedestrian recognition; both are typical classification problems from real-life applications. Finally, the model is compared with many other algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang4:2008:ijcnn,
author = "Rongbing Huang and Minghui Du and Dexin Xie",
title = "A Human Face Recognition Approach Based on Spatially Weighted Pseudo-Zernike Moments",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0517.pdf},
url = {},
size = {},
abstract = {A new modified pseudo-Zernike moments feature, namely, "spatial weighted pseudo- Zernike moments" (SWPZM) is proposed for face recognition in this paper. Since different facial region plays a different important role for face recognition, the new modified pseudo-Zernike feature is weighted with a weight function derived from the spatial information of the human face; hence the most important regions such as the eyes, nose, and mouth regions are intensified for face discrimination. Experimental results based on the AT&T/ORL, Yale, and their combined face database show that SWPZM can obtain 95.7percent, 92.3percent, and 92.5percent recognition rates with the nearest neighbor rule and have better identification power than other methods. },
keywords = {:Face recognition, pseudo-Zernike moments, feature extraction, principal component analysis, nearest neighbor.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hirose:2008:ijcnn,
author = "Akira Hirose ",
title = "An Adaptive Ground Penetrating Radar Imaging System Based on Complex-Valued Self-Organizing Map — Recent Progress and
Experiments in Cambodia",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0519.pdf},
url = {},
size = {},
abstract = {This paper reports recent progress in an adaptive ground penetrating radar imaging system based on a complexvalued neural network (CVNN), i.e., a complex-valued selforganizing map (CSOM). In the CSOM processing, we deal with feature vectors that represent complex-amplitude texture in space and frequency domains. We developed a switched walled linearly tapered slot antenna (walled-LTSA) array for the frontend. A higher resolution results in a better classification quality. To realize a high resolution in range and azimuth directions, we use a wide frequency bandwidth in frequency stepping operation, and a special switching scheme for the walled LTSA. We conducted experiments in Cambodia. In this paper, we report successful plastic landmine visualization, not only for targets buried in normal sand but also for those in wet laterite soil at the Siem Reap test site. Adaptive coherent radar imaging is one of the most potential application fields of the CVNNs. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen10:2008:ijcnn,
author = "Yajie Chen and Liam McDaid and Steve Hall and Peter Kelly",
title = "A Programmable Facilitating Synapse Device",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0520.pdf},
url = {},
size = {},
abstract = {We present a programmable dynamic Charge Transfer Synapse (CTS) in a single semiconductor device. The CTS comprises a Metal Oxide Semiconductor (MOS) transistor operating in subthreshold and two MOS capacitors in proximity to the transistor. One of the capacitors is permanently biased in strong inversion where the associated density of charge in the well implements the weighting. When a presynaptic spike is applied to the gate of the second MOS capacitor the charge density in the well falls producing a current spike at the output. The amplitude of the spike is correlated with the equilibrium charge density in the well, which is controlled by the associated gate voltage. Aggregation of spikes from an array of CTSs is achieved by using a current mirror configuration whose output postsynaptic potential can be used to stimulate a point neuron circuit. The function of the MOS transistor is to restore the charge in the well where the duration of this process is dictated by the associated gate voltage. Therefore, the synapse is capability of operating in the facilitating state over a large frequency range. The CTS is compact and since it operates in transient mode, its power consumption is negligible. Simulation results are presented which clearly demonstrate its operation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bologna:2008:ijcnn,
author = "G. Bologna and B. Deville and M. Vinckenbosch and T. Pun",
title = "A Perceptual Interface for Vision Substitution in a Colour Matching Experiment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0523.pdf},
url = {},
size = {},
abstract = {In the context of vision substitution by the auditory channel several systems have been introduced. One such system that is presented here, See Colour, is a dedicated interface part of a mobility aid for visually impaired people. It transforms a small portion of a coloured video image into spatialized instrument sounds. In this work the purpose is to verify the hypothesis that sounds from musical instruments provide an alternative way to vision for obtaining colour information from the environment. We introduce an experiment in which several participants try to match pairs of coloured socks by pointing a head mounted camera and by listening to the generated sounds. Our experiments demonstrated that blindfolded individuals were able to accurately match pairs of coloured socks. The advantage of the See Colour interface is that it allows the user to receive a feedback auditory signal from the environment and its colours, promptly. Our perceptual auditory coding of pixel values opens the opportunity to achieve more complicated experiments related to vision tasks, such as perceiving the environment by interpreting its colours. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ho:2008:ijcnn,
author = "Charlotte Yuk Fan Ho and Bingo Wing Kuen Ling and Muhammad H U Nasir and Hak Keung Lam",
title = "Properties of an Invariant Set of Weights of Perceptrons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0524.pdf},
url = {},
size = {},
abstract = {In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set. Hence, the invariant set is attracting. Computer numerical simulation results on various perceptrons with exhibiting various behaviors, such as fixed point behaviors, limit cycle behaviors and chaotic behaviors, are illustrated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
abstract = {Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here we summarize some preliminary results from the application of this integrated approach to robust speaker identification and speech recognition. A real-time stand-alone software prototype has been developed to evaluate the effectiveness of the approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kwan:2008:ijcnn,
author = "C. Kwan and S. Chu and J. Yin and X. Liu and M. Kruger and I. Sityar",
title = "Enhanced Speech in Noisy Multiple Speaker Environment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0526.pdf},
url = {},
size = {},
abstract = {Noisy environments seriously degrade the performance of speech recognition systems. Here we implement a high performance speech enhancement algorithm. Data from Speech Separation Challenge [1] were used to evaluate the method. It was observed that the enhanced speech significantly improved the recognition performance. In 2 out of 4 SNR cases, over 100percent relative percentage improvements were achieved. Standalone software prototype has been developed and evaluated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kwan2:2008:ijcnn,
author = "C. Kwan and J. Yin and B. Ayhan and S. Chu and X. Liu",
title = "Speech Separation Algorithms for Multiple Speaker Environments",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0527.pdf},
url = {},
size = {},
abstract = {Conventional speaker identification and speech recognition algorithms do not perform well if there are multiple speakers in the background. For high performance speaker identification and speech recognition applications in multiple speaker environments, a speech separation stage is essential. Here we summarize the implementation of three speech separation techniques. Advantages and disadvantages of each method are highlighted, as no single method can work under all situations. Stand-alone software prototypes for these methods have been developed and evaluated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ferreira2:2008:ijcnn,
author = "Aida A. Ferreira and Teresa B. Ludermir and Ronaldo R. B. de Aquino",
title = "Investigating the Use of Reservoir Computing for Forecasting the Hourly Wind Speed in Short-Term",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0529.pdf},
url = {},
size = {},
abstract = {This paper presents the results of the models created for forecasting the hourly wind speed in 24-step-forward using Reservoir Computing (RC). RC is a new paradigm that offers an intuitive methodology for using the temporal processing power of recurrent neural networks (RNN) without the inconvenience of training them. Originally, introduced independently as Liquid State Machine [5] or Echo State Network [6], whose basic concept is randomly construct a RNN and leave the weights unchanged. In this work we used Echo State Network (ESN) to create the models and Multi-Layer Networks (MLP) to compare the results. The results showed that the ESN performed significantly better than MLP networks, even though it presents a significantly simpler, and faster, training algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Baruch:2008:ijcnn,
author = "Ieroham S. Baruch and Rosalba Galvan-Guerra and Carlos-Roman Mariaca-Gaspar and Patricia Melin",
title = "Decentralized Indirect Adaptive Fuzzy-Neural Multi-Model Control of a Distributed Parameter Bioprocess Plant",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0530.pdf},
url = {},
size = {},
abstract = {The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are implemented by a Hierarchical Fuzzy-Neural Multi-Model Sliding Mode Controller (HFNMM-SMC). The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking outperforming the optimal control. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hu2:2008:ijcnn,
author = "Yunlong Hu and Yongchen Li",
title = "LS-SVM for Bad Debt Risk Assessment in Enterprises",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0531.pdf},
url = {},
size = {},
abstract = {With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes- normality, attention, doubt and loss through analyzing accounts payable. Then the LS-SVM classifier is trained with 220 samples which are stochastically extracted from listed companies of China in industry, and the four classes are identified by the trained classifier using 80 samples. Then, BP neural network is also used to assess the same data. The experiment results show that LS-SVM has an excellent performance on training accuracy and reliability in credit risk assessment and achieves better performance than BP neural network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang7:2008:ijcnn,
author = "Tianhao Zhang and Dacheng Tao and Xuelong Li",
title = "A Unifying Framework for Spectral Analysis Based Dimensionality Reduction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0532.pdf},
url = {},
size = {},
abstract = {Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., "patch alignment", which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Islam:2008:ijcnn,
author = "Atiq Islam and Khan M. Iftekharuddin and E. Olusegun. George",
title = "Class Specific Gene Expression Estimation and Classification in
Microarray Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0536.pdf},
url = {},
size = {},
abstract = {In this work, we characterize genes using an Oligonucleotide Affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior distributions of the class-specific gene expressions. We also formalize a novel class prediction scheme based on the samples from new posterior distributions of group specific gene expressions. Our experimental results show the classdiscriminating power of the selected genes. Furthermore, we demonstrate that our prediction scheme classifies tissue samples into appropriate treatment groups with high accuracy. The computations are implemented by using Gibbs sampling. We compare the efficacy of our proposed gene selection and prediction methods with that of Pomeroy et al. (Nature, 2002) on the same CNS tumor sample dataset. },
keywords = {: Affymetrix, hierarchical Bayesian, ANOVA model, clustering, CNS tumor, Gibbs sampling, marker genes,
parallel coordinate, Semantic Gene Organizer.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Amiri:2008:ijcnn,
author = "Mahmood Amiri and Mohammad Bagher Menhaj and Mohammad Javad Yazdanpanh",
title = "A Neural-Network-Based Controller for a Single-Link Flexible Manipulator: Comparison of FFNN and DRNN Controllers",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0538.pdf},
url = {},
size = {},
abstract = {This paper employs two types of neural networks to control a single-link flexible arm. To train each network, we use a gradient-based approach with adaptive learning rate. We first apply the Diagonal Recurrent Neural Network (DRNN) to a single-link flexible arm, which is a challenging control problem, in order to investigate the ability of this type of recurrent neural network. We then apply a feed-forward neural network (FFNN) to this problem and perform some case studies for the purpose of performance comparisons of the two structures. Several simulations presented in this paper verify that the DRNN-based controller significantly improves the precision of the tip motion tracking, suppresses the tip deflections of the manipulator more effectively and simultaneously produces more appropriate control voltages. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Khanmohammadi:2008:ijcnn,
author = "S. Khanmohammadi and H. Ghadiri",
title = "Modeling of Helper Robots in Manufacturing Systems Using Petri Nets",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0539.pdf},
url = {},
size = {},
abstract = {In this paper the Timed Petri Net (TPN) is used for modeling the operations of co-operative robots in Flexible Manufacturing Systems (FMS). The effect of fault parameters and the number of co-operative robots -we call them the helpers- in the performance of the system is investigated. These special purpose robots are used to decrease the operation times, as well as to increase the total performance of the system. These robots perform the preliminary tasks on the waiting jobs (parts) in the wait lines or input buffers of machines. Also some Automatic Guided Vehicles (AGVs) are used as material handlers between machines while performing some simple preliminary tasks during the material handling. Some sub- TPNs are used for modeling of FMS and helper robots, as special purpose robots. Simulation results show that by using the helpers, there will be a decrease of 23.7 percent in mean operation time of system, and a decrease of 25.4 percent in the mean waiting time of jobs at input buffers. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Davande:2008:ijcnn,
author = "Hamed Davande and Mahmood Amiri and Alireza Sadeghian and Sylvain Chartier",
title = "Auto-Associative Memory Based on a New Hybrid Model of SFNN and GRNN: Performance Comparison with NDRAM, ART2 and MLP",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0540.pdf},
url = {},
size = {},
abstract = {Currently, associative neural networks (AsNNs) are among the most extensively studied and understood neural paradigms. In this paper, we use a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model which consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN) were first proposed by authors of this paper. Firstly, patterns are stored as the asymptotically stable fixed points of the SFNN. In the retrieving process, each new pattern is applied to the GRNN to make the corresponding initial conditions of that pattern which initiate the dynamical equations of the SFNN. In this way, the corresponding stored patterns and noisy version of them are retrieved. Several simulations are provided to show that the performance of the hybrid model is better than those of recurrent associative memory, feed-forward multilayer perceptron and is equally comparable with the performance of hard-competitive models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Khanmohammadi2:2008:ijcnn,
author = "S. Khanmohammadi and A. Mahdizadeh",
title = "A New Technique for Optimizing and Smoothing Randomized Paths",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0541.pdf},
url = {},
size = {},
abstract = {In this paper we propose a new technique which uses a combination of two powerful path planning methods, in order to gain a fast path planning technique, which benefit's the advantages of both methods. First a feasible path is generated by a randomized path planner, and then it is leaded to a sub-optimal path by using Distance Transform in a bounded area. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Rong:2008:ijcnn,
author = "Hai-Jun Rong and Guang-Bin Huang and Yew-Soon Ong",
title = "Extreme Learning Machine for Multi-Categories
Classification Applications",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0543.pdf},
url = {},
size = {},
abstract = {In the paper, the multi-class pattern classification using extreme learning machine (ELM) is studied. The study is based on either a series of ELM binary classifiers or a single ELM classifier. When using binary ELM classifiers, the multi-class problem is decomposed into two-class problem using the one-against-all (OAA) and one-against-one (OAO) schemes, which are named as ELM-OAA and ELM-OAO respectively for brevity. In a single ELM classifier, the multi-class problem is implemented with an architecture of multi-output nodes which is equal to the number of pattern classes. Their performance is evaluated using some multi-class benchmark problems and simulation results show that ELM-OAA and ELM-OAO requires fewer hidden nodes than the single ELM classifier. In addition ELM-OAO usually has similar or less computation burden than the single ELM classifier when the pattern class labels is not larger than 10. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ye:2008:ijcnn,
author = "Mao Ye and Yongguo Liu and Hong Wu and Qihe Liu",
title = "A Few Online Algorithms for Extracting Minor Generalized Eigenvectors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0544.pdf},
url = {},
size = {},
abstract = {Recently, a few adaptive algorithms for generalized eigen-decomposition have been proposed, which are very useful in many applications such as digital mobile communications, Blind signal separation, etc. These algorithms are all focusing on extracting principal generalized eigenvectors. However, in many practical applications such as dimension reduction and signal processing, extracting the minor generalized eigenvectors adaptively are needed. Because of little literatures in the community, we discuss several approaches that lead to a few novel algorithms for extracting minor generalized eigenvectors. First, we derive an adaptive algorithms by using a singlelayer linear forward neural network from the viewpoint of linear discriminant analysis(LDA). And the algorithm to extract multiple minor generalized eigenvectors are also proposed by using orthogonality property. Second, by using gradient ascent approach of some objective functions, we can derive more algorithms and explain the first algorithm. Then, we extend these algorithms to minor generalized eigenvector problem. Theoretical analysis shows that these algorithms are stable and convergent to the minor generalized eigenvectors. Simulations have been conducted for illustration of the efficiency and effectiveness of our algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tan:2008:ijcnn,
author = "Tuan Zea Tan and Gary Kee Khoon Lee and Shie-Yui Liong and Tian Kuay Lim and and Terence Hung ",
title = "Rainfall Intensity Prediction by a Spatial-Temporal Ensemble",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0546.pdf},
url = {},
size = {},
abstract = {Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry periods in between raining seasons. Hence, conventional computational intelligence tools sometimes are not able to offer acceptable accuracy. An ensemble constitutes of classification, regression and reward models is proposed. The classification model identifies rain or no rain episodes, whereas the regression model predicts the rainfall intensity. The error of the regression model is then predicted by the reward regression model. Through that, the spatial information is captured by the classification model, and the temporal information is captured by the regression and reward models. Preliminary experimental results are encouraging. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tadeusiewicz:2008:ijcnn,
author = "Ryszard Tadeusiewicz and Marek R. Ogiela",
title = "Medical Pattern Understanding Based on Cognitive Linguistic Formalisms and Computational Intelligence Methods",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0547.pdf},
url = {},
size = {},
abstract = {This paper will present the achievements of its authors related to the development of new cognitive information systems classes used in the tasks of automatic understanding of image data semantics. Such systems are a practical implementation of the paradigm for machine semantics understanding of selected image data types, with special regards to various classes of medical images. The development of such systems is possible owing to defining computer procedures of cognitive resonance, as used by the developed new classes of intelligent systems for pattern recognition and semantic reasoning. In particular, we shall present UBIAS (Understanding Based Image Analysis Systems) system classes, proposed by the authors for the interpretation of medical planar diagnostic images and for the modelling of spatial anatomy structures. },
keywords = {: computational intelligence, medical pattern understanding, image recognition, mathematical linguistic,
cognitive reasoning},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tan2:2008:ijcnn,
author = "Wi-Meng Tan and Hiok-Chai Quek",
title = "Adaptive Training Schema in Mamdani-Type Neuro-Fuzzy Models for Data-Analysis in Dynamic System Forecasting",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0549.pdf},
url = {},
size = {},
abstract = {This paper investigates the possibility of a pseudoonline adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics of time-varying dynamic systems for the purpose of forecasting. Experimental results showed that the proposed model is capable of adapting its rule base over time, and uses a relatively fewer number of rules for generalization in dynamic systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gromiha:2008:ijcnn,
author = "M. Michael Gromiha and Shandar Ahmad",
title = "Neural Network based Prediction of Protein Structure and Function: Comparison with Other Machine Learning Methods",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0550.pdf},
url = {},
size = {},
abstract = {We have used neural networks in different applications of bioinformatics such as discrimination of β-barrel membrane proteins, mesophilic and thermophilic proteins, different folding types of globular proteins, different classes of transporter proteins and predicting the secondary structures of β-barrel membrane proteins. In these methods, we have used the information about amino acid composition, neighboring residue information, inter-residue contacts and amino acid properties as features. We observed that the performance with neural networks is comparable to or better than other widely used machine learning techniques. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Andrabi:2008:ijcnn,
author = "Munazah Andrabi and Shandar Ahmad and Kenji Mizuguchi and Akinori Sarai",
title = "Benchmarking and Analysis of DNA-Binding Site Prediction
Using Machine Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0554.pdf},
url = {},
size = {},
abstract = {We benchmarked the performance of machine learning based publicly available web servers, predicting DNA-binding residues. A blind test on data sets derived from protein-DNA complexes submitted to PDB after the publication of these web servers was performed. It was discerned that models trained on unusually large number of parameters show exaggerated performance during training, which could not be sustained on new proteins submitted after these publications. Also discussed are the optimum definition of binding site and a correspondence between residue propensity and its bias for predictability in positive and negative class. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pampara:2008:ijcnn,
author = "G. Pampara and A. P. Engelbrecht and T. Cloete",
title = "CIlib: A Collaborative Framework for Computational Intelligence Algorithms — Part I",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0555.pdf},
url = {},
size = {},
abstract = {Research in Computational Intelligence (CI) has produced a huge collection of algorithms, grouped into the main CI paradigms. Development of a new CI algorithm requires such algorithm to be thoroughly benchmarked against existing algorithms, which requires researchers to implement already published algorithms. This re-implementation of existing algorithms unnecessarily wastes valuable time, and may be the cause of incorrect results due to unexpected bugs in the code. It is also the case that more, new CI algorithms are hybrids of algorithms from different paradigms. This illustrates a demand for a comprehensive library of CI algorithms, to minimize development time and the occurrence of programming errors, and to facilitate combination of components to form hybrid models. This paper presents such a library, called CIlib. },
keywords = {genetic algorithms, genetic programming},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zheng2:2008:ijcnn,
author = "Chaoxin Zheng and Dermot Kelleher and Khurshid Ahmad",
title = "A Semi-Automatic Indexing System for Cell Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0556.pdf},
url = {},
size = {},
abstract = {A method is described that can be used for annotating and indexing an arbitrary set of images with texts collateral to the images. The collateral texts comprise digitised texts, e.g. journal papers and newspapers in which the images appear, and digitised speech, e.g. a commentary on the contents of the images. The annotation 'vector' comprises image features and keywords in the collateral texts; our method can be used to generate both the image features and keywords automatically. Terminology extraction techniques are incorporated into the system to form a domain specific lexicon, which can then be used or help to annotate the images. Our method can be used as the basis of the autonomous learning of associations between images and their collateral descriptions, for example using Kohonen feature maps. We focus on images that show the migration and the division of cells within live systems. We show how the annotations can be collected by using the state-of-the-art speech recognition techniques that convert audio input into descriptive text on cell migration. A system based on the method has been developed and has reduced the annotation time to around two minutes per image, on a set of 429 cell images — which is significantly smaller than 5 minutes for manual annotation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cloete:2008:ijcnn,
author = "T. Cloete and A. P. Engelbrecht and G. Pampara",
title = "CIlib: A Collaborative Framework for Computational Intelligence Algorithms — Part II",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0560.pdf},
url = {},
size = {},
abstract = {CIlib is a recently developed open source library of Computational Intelligence (CI) algorithms. Developed in Java, and designed to be a generic framework of pluggable components, CIlib provides the CI researcher with a powerful tool to facilitate research in new CI techniques, and to easily benchmark against existing CI algorithms on a variety of problems. Consisting of a number of frameworks, including a framework for most CI paradigms, CIlib also allows components from different frameworks to be weaved together to form hybrid CI models. This paper provides a detailed illustration of how CIlib can be used to easily setup simulations using different algorithms to solve various problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fontanari:2008:ijcnn,
author = "Jose F. Fontanari and and Leonid I. Perlovsky",
title = "Object Perception in the Neural Modeling Fields Framework",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0561.pdf},
url = {},
size = {},
abstract = {Movement seems to be the key ingredient to understanding how children perceive (and hence name) objects in their environment. This insight, which is based on Spelke's experiments on language acquisition by children, is akin to Aristotle's conception of object as a "continuous thing" that has one and only one movement. Here we test this idea with a computer experiment in which the perceptual system of the individual is modeled by the Neural Modeling Fields categorization mechanism, and the environment is composed of two complex objects that can move with respect to each other. Rather remarkably, we find that movement indeed makes possible the differentiation of objects which the individual would deem indistinguishable if motionless. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ferrari:2008:ijcnn,
author = "Silvia Ferrari and Bhavesh Mehta and Gianluca Di Muro and Antonius M. J. VanDongen and Craig Henriquez",
title = "Biologically Realizable Reward-Modulated Hebbian Training for Spiking Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0562.pdf},
url = {},
size = {},
abstract = {Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximators. Spiking neural networks offer several advantages over sigmoidal networks, because they can approximate the dynamics of biological neuronal networks, and can potentially reproduce the computational speed observed in biological brains by enabling temporal coding. On the other hand, the effectiveness of spiking neural network training algorithms is still far removed from that exhibited by backpropagating sigmoidal neural networks. This paper presents a novel algorithm based on reward-modulated spike-timing-dependent plasticity that is biologically plausible and capable of training a spiking neural network to learn the exclusive-or (XOR) computation, through rate-based coding. The results show that a spiking neural network model with twenty-three nodes is able to learn the XOR gate accurately, and performs the computation on time scales of milliseconds. Moreover, the algorithm can potentially be verified in light-sensitive neuronal networks grown in vitro by determining the spikes patterns that lead to the desired synaptic weights computed in silico when induced by blue light in vitro. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fiori:2008:ijcnn,
author = "Simone Fiori ",
title = "Generation of Pseudorandom Numbers with Arbitrary Distribution by Learnable Look-Up-Table-Type Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0564.pdf},
url = {},
size = {},
abstract = {The aim of the present manuscript is to propose a pseudo-random number generation algorithm based on a learnable non-linear neural network whose implementation is based on look-up tables. The proposed neural network is able to generate pseudo-random numbers with arbitrary distribution on the basis of standard variate generators available within programming environments. The proposed method is not computationally demanding and easy to implement on a computer. Numerical tests confirm the agreement between the desired and obtained distributions of the generated pseudo-random number batches. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang8:2008:ijcnn,
author = "Yu-Chiang Frank Wang and David Casasent",
title = "Soft-Decision Hierarchical Classification Using
SVM-Type Classifiers",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0565.pdf},
url = {},
size = {},
abstract = {In this paper, we address both recognition of true object classes and rejection of false (non-object) classes as occurs in many realistic pattern recognition problems. We modified our hierarchical binary-decision classifier to produce analog outputs at each node, with values proportional to the class conditional probabilities at that node. This yields a new soft-decision hierarchical system. The hierarchical classification structure is designed by our weighted support vector k-means clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. Compared to the standard SVM, use of the Gaussian kernel function and a looser constraint in the classifier design give our SVRDM an improved rejection ability. The soft-decision SVRDM output allows us to use the confidence level of each class to improve the classification (for true class inputs) and rejection (for false class inputs) performance of the hierarchical classifier. False class rejection is a major new aspect of this work. It is not present in most prior work. Excellent test results on a real infra-red (IR) database are presented. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fiori2:2008:ijcnn,
author = "Simone Fiori ",
title = "Learning Stepsize Selection for the Geodesic-Based Neural Blind Deconvolution Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0566.pdf},
url = {},
size = {},
abstract = {The present paper illustrates a geodesic-based learning algorithm over a curved parameter space for blind deconvolution application. The chosen deconvolving structure appears as a single neuron model whose learning rule arises from criterion-function minimization over a smooth manifold. In particular, we propose here a learning stepsize selection theory for the algorithm at hand. We consider the blind deconvolution performances of the algorithm as well as its computational burden. Also, a numerical comparison with seven blind-deconvolution algorithms known from the scientific literature is illustrated and discussed. Results of numerical tests conducted on a noiseless as well as a noisy system will confirm that the algorithm discussed in the present paper performs in a satisfactory way. Also, the performances of the presented algorithm will be compared with those exhibited by other blind deconvolution algorithms known from the literature. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu6:2008:ijcnn,
author = "Wen Yu and Xiaoou Li",
title = "Robust Adaptive Control Via Neural Linearization and Four Types of Compensation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0567.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifierbased feedback linearization controller is first used. Deadzone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Woodside:2008:ijcnn,
author = "Joseph M. Woodside ",
title = "Neuro-Fuzzy CBR Hybridization: Healthcare Application",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0568.pdf},
url = {},
size = {},
abstract = {As the total cost of healthcare continues to rise, computerized methods are sought to improve the overall efficiency and effectiveness of healthcare systems. In this application, the focus is on healthcare claim payment processing, which is a major component of administrative healthcare costs. Due to the complexity of healthcare data, current methods require a large amount of healthcare claim payment processing to occur through manual intervention by human operators. This limitation necessitates the inclusion of machine learning techniques to create a hybrid system for automation of healthcare claim payments. Further automation of claims payment processing will lead to improved quality cost components of healthcare delivery. Machine learning techniques are used to demonstrate the feasibility of a hybrid system for healthcare claim payment automation, leading to reduced administrative costs and increased efficiencies. When the administrative cost savings are applied to the industry, this contributes to lowering the overall cost of healthcare. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Castillo:2008:ijcnn,
author = "Oscar Castillo and Patricia Melin",
title = "Computational Intelligence Software: Type-2 Fuzzy Logic and Modular Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0569.pdf},
url = {},
size = {},
abstract = {This paper presents the development and design of two software tools for computational intelligence. The software tools include a graphical user interface for construction, edition and observation of the intelligent systems. The software tool are for Interval Type-2 Fuzzy Logic and Modular Neural Networks. The Interval Type-2 Fuzzy Logic System Toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the Toolbox. The Toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of Interval Type-2 Fuzzy Inference Systems. The toolbox for modular neural networks has similar advantages. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Choi:2008:ijcnn,
author = "Seungjin Choi ",
title = "Algorithms for Orthogonal Nonnegative Matrix Factorization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0570.pdf},
url = {},
size = {},
abstract = {Nonnegative matrix factorization (NMF) is a widely-used method for multivariate analysis of nonnegative data, the goal of which is decompose a data matrix into a basis matrix and an encoding variable matrix with all of these matrices allowed to have only nonnegative elements. In this paper we present simple algorithms for orthogonal NMF, where orthogonality constraints are imposed on basis matrix or encoding matrix. We develop multiplicative updates directly from the true gradient (natural gradient) in Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Numerical experiments on face image data for a image representation task show that our orthogonal NMF algorithm preserves the orthogonality, while the goodness-off-it (GOF) is minimized. We also apply our orthogonal NMF to a clustering task, showing that it works better than the original NMF, which is confirmed by experiments on several UCI repository data sets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Azcarraga:2008:ijcnn,
author = "Arnulfo P. Azcarraga and Aldrich C. Caw",
title = "Enhancing SOM Digital Music Archives Using
Scatter-Gather",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0571.pdf},
url = {},
size = {},
abstract = {The MarB system is a digital archive of music files that are clustered and laid out as a self-organized map, following the SOM methodology for large digital archives. The system has the usual music archive features as follows: (1) automatic clustering and organization of music files into "islands of related music"; (2) classification of music clusters into various music genres; (3) playback of music files selected by the user; and (4) automatic generation of related music files for every music file that is chosen. In addition to these rather common features found in most Self-Organizing Maps (SOM) based digital music archives, MarB also allows for an interactive selection and clustering of sets and subsets of music files until a specific music file is found. This is done using a Scatter/Gather interface that allows the user to select interesting clusters of music files (gather mode), which are then re-organized and re-clustered (scatter mode) for the user to visually inspect and possibly listen to. The user is then asked to select new interesting clusters (gather mode again). This alternating selection and re-clustering process continues until the user chooses a specific music file, and is provided with a set of most related music files. A novel album dispersal measure is used to objectively assess the quality of the clusters produced both by the SOM and the special k-means algorithm employed in the Scatter-Gather module. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li6:2008:ijcnn,
author = "Tao Li and Dongbin Zhao and Jianqiang Yi",
title = "Adaptive Dynamic Neuro-Fuzzy System for Traffic Signal Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0574.pdf},
url = {},
size = {},
abstract = {This paper aims at developing near optimal traffic signal control for multi-intersection in city. Fuzzy control is widely used in traffic signal control. For improving fuzzy control's adaptability in fluctuant states, a controller combined with neuro-fuzzy system and adaptive dynamic programming (ADP) is designed. This controller can be used for cooperative control of multi-intersection. The adaptive dynamic programming gives reinforcement for good neuro-fuzzy system behavior and punishment for poor behavior. The neuro-fuzzy system adjusts its parameters according to the reinforcement and punishment. Then, those actions leading to better results tend to be chosen preferentially in the future. Comparing with traditional ADP, this controller uses neuro-fuzzy system as the action network. The neuro-fuzzy system offers some existing knowledge and reduces the randomness of traditional ADP. In this paper, the objective of the controller is to minimize the average vehicular delay. The controller can be trained to adapt fluctuant traffic states by real-time traffic data, and achieves a near optimal control result in a long run. Simulation results show that the trained controller achieves shorter average vehicular delay than the controller with initial membership function. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bai2:2008:ijcnn,
author = "Xuerui Bai and Dongbin Zhao and Jianqiang Yi",
title = "Ramp Metering Based on On-Line ADHDP (λ) Controller",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0575.pdf},
url = {},
size = {},
abstract = {Increasing dependence on car-based travel has led to the daily occurrence of freeway congestions around the world. In order to improve the worse and worse traffic congestion situation and solve the problems brought with it, a new kind of effective, fast, and robust method should be presented. Ramp metering has been developed as a traffic management strategy to alleviate congestion on freeways. But, it doesn't work well in uncertainty situations. In this paper, in order to solve the problems in uncertainty conditions, an on-line learning control method based on the fundamental principle of reinforcement learning is proposed. The method is ADP (adaptive dynamic programming) and in order to expedite the learning rate, the concept about eligibility traces is introduced here. Then eligibility trace and ADP is combined to present a new kind of traffic responsive control method. The new method is called action-dependent heuristic dynamic programming based on eligibility traces (ADHDP (λ)). ADHDP (λ) is an approximate optimal ramp metering method. Simulation studies on a hypothetical freeway indicate good control performance of the proposed real-time traffic controller. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li7:2008:ijcnn,
author = "Wei Li and Yannan Zhao and Yixu Song and Zehong Yang",
title = "COX-2 Activity Prediction in Chinese Medicine Using Neural Network Based Ensemble Learning Methods",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0576.pdf},
url = {},
size = {},
abstract = {In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine Quantitative Structure-Activity Relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting, are fast and effective ways in the activity prediction of Chinese medicine QSAR research, which is generally based on a small amount of training samples. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lan:2008:ijcnn,
author = "Yuan Lan and Yeng Chai Soh and Guang-Bin Huang",
title = "Extreme Learning Machine Based Bacterial Protein Subcellular Localization Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0577.pdf},
url = {},
size = {},
abstract = {In this paper, Extreme Learning Machine (ELM) is introduced to predict the subcellular localization of proteins based on the frequent subsequences. It is proved that ELM is extremely fast and can provide good generalization performance. We evaluated the performance of ELM on four localization sites with frequent subsequences as the feature space. A new parameter called Comparesup was introduced to help the feature selection. The performance of ELM was tested on data with different number of frequent subsequences, which were determined by different range of Comparesup. The results demonstrated that ELM performed better than previously reported results, for all of the four localization sites. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Papadopoulos:2008:ijcnn,
author = "George Papadopoulos and Martin Brown",
title = "Minimum Entropy Parameter Estimation: Application to the RKIP Regulated ERK Signaling Pathway",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0579.pdf},
url = {},
size = {},
abstract = {Parameter estimation plays an important role in Systems Biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding to a lower entropy measure on the residual joint probability density function. The residual joint PDF is approximated using Kernel Density Estimation methods and the method is designed to handle general multivariable dynamic ODE systems where the measurement noise is not necessarily Gaussian. The analysis on the proposed minimum entropy parameter estimation involves an application to the RKIP regulated ERK pathway where the demonstrated simulation results clearly indicate its effectiveness. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Beran:2008:ijcnn,
author = "Peter Paul Beran and Elisabeth Vinek and Erich Schikuta and Thomas Weishäupl",
title = "ViNNSL — The Vienna Neural Network Specification Language",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0580.pdf},
url = {},
size = {},
abstract = {We present ViNNSL (Vienna Neural Network Specification Language), an XML based language for describing, training and running neural networks on a Grid infrastructure. ViNNSL allows for dynamic client-server communication regarding the semantics of neural network resources. As a proof-of-concept the language is used in N2Grid, a system for the use of neural network resources on a worldwide basis. Based on the Grid infrastructure N2Grid allows to build up a virtual community enabling arbitrary users to exchange knowledge (neural network resources, such as neural network objects and neural network paradigms) and to exploit the available computing resources for neural network specific tasks, leading to a Grid based, world-wide distributed, neural network knowledge and simulation system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu8:2008:ijcnn,
author = "Yong Liu ",
title = "Reduction of Difference among Trained Neural Networks by Re-Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0581.pdf},
url = {},
size = {},
abstract = {It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances for learned neural networks by letting them re-learn on those data points on which they disagrees with each other. Experimental results have been conducted on four real world applications to explain how and when such re-learning works. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chandrakala:2008:ijcnn,
author = "S. Chandrakala and C. Chandra Sekhar",
title = "A Density based Method for Multivariate Time Series Clustering in Kernel Feature Space",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0583.pdf},
url = {},
size = {},
abstract = {Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel k-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are mutivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang4:2008:ijcnn,
author = "Bingbing Yang and Qian Yin and Shengyong Xu and Ping Guo",
title = "Software Quality Prediction Using Affinity Propagation Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0585.pdf},
url = {},
size = {},
abstract = {Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered as one of the data mining techniques, is adopted to build the software quality prediction models in the early period of software testing. In this paper, a new clustering method called Affinity Propagation is investigated for the analysis of two software metric datasets extracted from real-world software projects. Meanwhile, K-Means clustering method is also applied for comparison. The numerical experiment results show that the Affinity Propagation algorithm can be applied well in software quality prediction in the very early stage, and it is more effective on reducing Type II error. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jin2:2008:ijcnn,
author = "Feng Jin and Shiliang Sun",
title = "Neural Network Multitask Learning for Traffic Flow Forecasting",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0586.pdf},
url = {},
size = {},
abstract = {Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic flow forecasting. For neural network MTL, a backpropagation (BP) network is constructed by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output layer can be seen as outputs of different but closely related STL tasks. Comprehensive experiments on urban vehicular traffic flow data and comparisons with STL show that MTL in BP neural networks is a promising and effective approach for traffic flow forecasting. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Oentaryo:2008:ijcnn,
author = "Richard J. Oentaryo and Michel Pasquier ",
title = "Towards A Novel Integrated Neuro-Cognitive Architecture (INCA)",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0587.pdf},
url = {},
size = {},
abstract = {Artificial intelligence research is now flourishing which aims at achieving general, human-level intelligence. Accordingly, cognitive architectures are increasingly employed as blueprints for building intelligent agents to be endowed with various perceptive and cognitive abilities. This paper presents a novel Integrated Neuro-Cognitive Architecture (INCA) which emulate the putative functional aspects of various salient brain sub-systems via a learning memory modeling approach. The strength of INCA lies in self-organizing connectionist learning to induce high-level symbolic knowledge autonomously, and support for meta-cognitive functions. Its overall operations are governed by its consolidation and inference cycles, which posit a human-plausible way for forming and exploiting knowledge. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang5:2008:ijcnn,
author = "Shian-Chang Huang and Tung-Kuang Wu",
title = "Forecasting Stock Indices with Wavelet-based Kernel Partial
Least Square Regressions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0589.pdf},
url = {},
size = {},
abstract = {This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is used as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model is employed to create the most efficient subspace that keeping maximum covariance between inputs and outputs, and perform the final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang5:2008:ijcnn,
author = "Yingjie Yang and Chris Hinde and David Gillingwater",
title = "Airport Noise Simulation Using Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0591.pdf},
url = {},
size = {},
abstract = {Aircraft noise is influenced by many complex factors and it is difficult to devise an accurate mathematical model to simulate it with respect to operations at an airport. This paper presents an investigation in simulating airport noise using artificial neural networks. The results show that it is possible to establish a simple neural network model with monitored data for a specific airport and specific aircraft under local conditions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Vassilas:2008:ijcnn,
author = "Nikolaos Vassilas ",
title = "Batch Self-Organizing Map Algorithm: A Theoretical Study of
Self-Organization of a 1-D Network Under Quantization Effects",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0592.pdf},
url = {},
size = {},
abstract = {In this paper, we examine necessary and sufficient conditions that ensure self-organization of the batch variant of the self-organizing map algorithm for 1-D networks and for quantized weights and inputs. Using Markov chain formalism, it is shown that the existing analysis for the original algorithm can be extended to also include the more general batch variant. Finally, simulations verify the theoretical results, relate the speed of weight ordering to the distribution of the inputs and show the existence of metastable states of the Markov chain. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Castañeda:2008:ijcnn,
author = "Carlos E. Castañeda and Edgar N. Sanchez and Alexander G. Loukianov",
title = "Discrete-Time Recurrent Neural DC Motor Control using Kalman Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0593.pdf},
url = {},
size = {},
abstract = {An adaptive tracking controller for a discrete-time direct current (DC) motor model in presence of bounded disturbances is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding modes techniques are used to develop the reference tracking control. This paper includes also the respective stability analysis and a strategy to avoid specific adaptive weights zero-crossing. The scheme is illustrated via simulations for a discrete-time nonlinear model of an electric DC motor. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva:2008:ijcnn,
author = "Kelly P. Silva and Rodrigo G. F. Soares and Francisco A. T. de Carvalho and Teresa B. Ludermir",
title = "Evolving Both Size And Accuracy of RBF Networks Using Memetic Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0595.pdf},
url = {},
size = {},
abstract = {One of the main obstacles to obtain an artificial neural network with reasonable performance is the parameter setting. This work proposes a methodology to the automatic definition of RBF (Radial Basis Function) networks with an appropriate configuration for the selected classification problems. We propose the use of a Memetic Algorithm in order to perform the search for networks with minimum architecture and error rate. A set of experiments was made with four datasets and we were able to show the effectiveness of The method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Soares:2008:ijcnn,
author = "Rodrigo G. F. Soares and Kelly P. Silva and Teresa B. Ludermir and Francisco A. T. de Carvalho",
title = "An Evolutionary Approach for the Clustering Data Problem",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0597.pdf},
url = {},
size = {},
abstract = {The clustering problem consists in the discovery of interesting groups in a dataset. Such task is very important and widely tackled in the literature. In this paper, we propose an evolutionary method in order to obtain well formed and spatially separated clusters. The proposed algorithm uses a complete solution representation, each partition is represented by a length-variable chromosome. The variation operators were chosen to facilitate the exchange of clustering information between individuals.We have put two complementary clustering criteria together in the fitness function, so that the method can find clusters with arbitrary shapes. The k-means algorithm was the basis of the local search operator, such operator might refine the clustering solutions. The population diversity was an important issue for the algorithm, so a diversity maintenance scheme was employed. Differently from other existing clustering algorithms, our algorithm does not need the setting of the number of clusters in advance. We evaluated the method in different contexts, using both real and simulated data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zanchettin:2008:ijcnn,
author = "Cleber Zanchettin and Teresa B. Ludermir",
title = "Feature Subset Selection in a Methodology for Training and Improving Artificial Neural Network Weights and Connections",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0600.pdf},
url = {},
size = {},
abstract = {This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of Multi-Layer Perceptron neural networks. We compare the performance of the proposed method for feature subset selection to five classical feature selection methods in three different classification problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pan:2008:ijcnn,
author = "Hong Pan and W. C. Siu and N. F. Law ",
title = "Efficient and Low-Complexity Image Coding with the Lifting
Scheme and Modified SPIHT",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0602.pdf},
url = {},
size = {},
abstract = {In this paper, we propose an efficient and low complexity image coding algorithm based on the lifting wavelet transform and listless modified SPIHT (LWT-LMSPIHT). LWT-LMSPIHT jointly considers the advantages of progressive transmission and spatial scalability that were not fully provided by the SPIHT algorithm, thus it outperforms the SPIHT at low bit rates coding. The coding efficiency of LWT-LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information earlier in the bit stream belonging to the lower frequency bands than SPIHT to better exploit the energy compaction of the wavelet coefficients. Finally, a listless structure further reduces the amount of memory and improves the speed of compression by more than 47percent for a 512 × 512 image, as compared with the SPIHT algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fyfe:2008:ijcnn,
author = "Colin Fyfe and Tseng Wen-Ching and Wu Chia-Ti and Chien Shih-Yu and Pei Ling Lai",
title = "A Topology Preserving Mapping for Face Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0603.pdf},
url = {},
size = {},
abstract = {We review a recent form of topology preserving mapping which uses the same underlying structure as the Generative Topographic Mapping (GTM) but organises the projections of the latent points into data space based on the method of Harmonic K-means. We show that projections of the Olivetti Face Database onto this latent space show good performance in terms of identifying all images of a particular individual as lying in the same section of the latent space. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lam:2008:ijcnn,
author = "Benson S. Y. Lam and Hong Yan",
title = "Robust Clustering Algorithm for High Dimensional Data Classification based on Multiple Supports",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0604.pdf},
url = {},
size = {},
abstract = {High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly reduced. This is achieved by using multiple supports. Initially, a large support is used and then its size is reduced and eventually only a subgroup of data samples is considered for clustering. This procedure can filter out lots of contaminated information. Experiment results show that the proposed method effectively resolves all these problems. It outperforms existing ones for real world high dimensional datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tzortzis:2008:ijcnn,
author = "Grigorios Tzortzis and Aristidis Likas",
title = "The Global Kernel k-Means Clustering Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0606.pdf},
url = {},
size = {},
abstract = {Kernel k-means is an extension of the standard kmeans clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters and, due to its incremental nature and search procedure, locates near optimal solutions avoiding poor local minima. Furthermore a modification is proposed to reduce the computational cost that does not significantly affect the solution quality. We test the proposed methods on artificial data and also for the first time we employ kernel k-means for MRI segmentation along with a novel kernel. The proposed methods compare favorably to kernel k-means with random restarts. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen11:2008:ijcnn,
author = "Dan Chen and YueChao Wang and and XuSheng Tang",
title = "GPC Scheme for the Internet-Based Teleoperation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0607.pdf},
url = {},
size = {},
abstract = {The variable time delay and the packet loss degrade the performance of Internet based teleoperation system seriously, even make the system unstable. To overcome the trouble, an idea that using CARIMA model of the linearized slave robot to design a controller based on the Generalized Predictive Control (GPC) method is proposed in this paper. We place the GPC controller at the remote site. First of all, The CARIMA model of the linearized slave robot is derived. Secondly, a GPC controller is designed at slave site to generate the redundant control information to diminish the influence of the packet loss and the large time delay in the internet to the system. Moreover, in order to solve the problem caused by the variable time delay, the reference information signed with time stamp is used and fedback to the operator, so the operator can predict the next round trip time delay(RTT) according to the preceding RTT we got. Finally, stability condition is achieved. Simulation results show that these strategies can dynamically compensate for the variable time delay and reduce the performance degradation induced by packet loss. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhou2:2008:ijcnn,
author = "Bo Zhou and Jianda Han",
title = "Dynamic Feedback Tracking Control of Tracked Mobile Robots with Estimated Slipping Parameters",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0608.pdf},
url = {},
size = {},
abstract = {The trajectory tracking control problem of a tracked vehicle with slipping is considered in this paper. The slipping effects are analyzed and modeled as three time-varying parameters, which can be estimated simultaneously with robot's pose using nonlinear estimators such as unscented Kalman filter. Dynamic feedback linearization integrated with a globally exponential stabilizing state feedback is applied to achieve the tracking control objective. Simulation results are provided to demonstrate the effectiveness of proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tan3:2008:ijcnn,
author = "T. Z. Tan and G. S. Ng and C. Quek ",
title = "Improving Tractability of Clinical Decision Support System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0609.pdf},
url = {},
size = {},
abstract = {Clinical Decision Support System (CDSS) is a promising tool that can alleviate the high medical error rate. However, most of the CDSS are not adopted in clinical settings due to the lack of trust amongst the physicians. Thus, the development of CDSS should cater to the psychological need of physicians. One major issue preventing the wide acceptance of CDSS is the tractability of the system. Hence, in this paper, an attempt is made to improve the system tractability. One possible approach is proposed to improve the tractability of present CDSS. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Islam2:2008:ijcnn,
author = "Md. Monirul Islam and Md. Faijul Amin and Suman Ahmmed and Kazuyuki Murase",
title = "An Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0610.pdf},
url = {},
size = {},
abstract = {This paper presents a new algorithm, called adaptive merging and growing algorithm (AMGA), for designing artificial neural networks (ANNs). The new algorithm merges and adds hidden neuron during training. The merging operation introduced here is a kind mixed mode operation that is equivalent to pruning two neurons and adding one neuron. Unlike most previous studies on designing ANNs, AMGA puts emphasis on adaptive functioning in designing ANNs. This is the main reason why AMGA merges and adds hidden neurons repeatedly (or alternatively) based on the learning ability of hidden neurons and training progress of ANNs, respectively. AMGA has been tested on five benchmark problems including the Australian credit card, cancer, diabetes, glass and thyroid problems. The experimental results show that AMGA can produce ANNs with good generalization ability compared to other algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bozakov:2008:ijcnn,
author = "Zdravko Bozakov and Lars Graening and Stephan Hasler and Heiko Wersing and Stefan Menzel",
title = "Unsupervised Extraction of Design Components for a 3D parts-based Representation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0612.pdf},
url = {},
size = {},
abstract = {During CAD development and any kind of design optimisation over years a huge amount of geometries accumulate in a design department. To organize and structure these designs with respect to reusability, a hierarchical set of components on different scalings is extracted by the designers. This hierarchy allows to compose designs from several parts and to adapt the composition to the current task. Nevertheless, this hierarchy is imposed by humans and relies on their experiences. In the present paper a computational method is proposed for an unsupervised extraction of design components from a large repository of geometries. Methods known from the field of object and pattern recognition in images are transferred to the 3D design space to detect relevant features of geometries. The non-negative matrix factorization algorithm (NMF) is extended and tuned to the given task for an autonomous detection of design components. The results of the NMF additionally provide an overview on the distribution of these components in the design repository. The extracted components sum up in a parts-based representation which serves as a base for manual or computational design development or optimisation respectively. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Botteldooren:2008:ijcnn,
author = "Dick Botteldooren and Bert De Coensel",
title = "A Model for Long-Term Environmental Sound Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0613.pdf},
url = {},
size = {},
abstract = {In recent years, knowledge on primary processing of sound by the human auditory system has tremendously increased. This paper exploits the opportunities this creates for assessing the impact of (unwanted) environmental noise on quality of life of people. In particular the effect of auditory attention in a multisource context is focused on. The typical application envisaged here is characterized by very long term exposure (days) and multiple listeners (thousands) that need to be assessed. Therefore, the proposed model introduces many simplifications. The results obtained show that the approach is nevertheless capable of generating insight in the emergence of annoyance and the appraisal of open area soundscapes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shahjahan:2008:ijcnn,
author = "Md. Shahjahan and Md. Asaduzzaman and K. Murase",
title = "How to Maintain Information Content in Artificial Neural Networks with Coherence Adaptation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0616.pdf},
url = {},
size = {},
abstract = {This paper presents a learning approach called adaptive coherence scheme (CAS) that adaptively reduces information on input patterns in hidden layer(s) of a neural network. The hidden units in a neural network store information continuously during training session. As a result the network becomes extremely familiar with every details of input patterns. This is not desirable in training. Therefore, we attempt to limit this information automatically with a regularization function consisting of activations of hidden units. We proposed standard coherence learning (SCL) where a constant coherence strength was used in order to solve the problem. Here, we attempt to develop a coherence adaptation scheme in order to maintain small amount of information in the network automatically. We have applied the algorithm to the breast cancer classification and Mackey-Glass chaotic time series prediction problems with single and double hidden layered networks. The results show that the network maintains small amount of information with good classification and prediction accuracies. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Okun2:2008:ijcnn,
author = "Oleg Okun and Helen Priisalu",
title = "Ensembles of K-Nearest Neighbors and Dimensionality Reduction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0617.pdf},
url = {},
size = {},
abstract = {In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that such ensembles can yield both good accuracy and dimensionality reduction. If a characteristic called dataset complexity guides which random subset to include into an ensemble, then the ensemble achieves even better performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wong:2008:ijcnn,
author = "Max H. Y. Wong and Raymond S. T. Lee",
title = "Wind Shear Forecasting by Chaotic Oscillatory-Based Neural Networks (CONN) with Lee Oscillator (Retrograde Signalling) Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0618.pdf},
url = {},
size = {},
abstract = {Wind shear is a conventionally unpredictable meteorological phenomenon which presents a common danger to aircraft, particularly on takeoff and landing at airports. This paper describes a method for forecasting wind shear using an advanced paradigm from computational intelligence, Chaotic Oscillatory-based Neural Networks (CONN). The method uses weather data to predict wind velocities and directions over a short time period. This approach may have a wide variety of applications but from the aviation forecast perspective, it can be used in aviation to generate wind shear alerts. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liang2:2008:ijcnn,
author = "Lichen Liang and Vladimir Cherkassky",
title = "Connection Between SVM+ and Multi-Task Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0619.pdf},
url = {},
size = {},
abstract = {Exploiting additional information to improve traditional inductive learning is an active research in machine learning. When data are naturally separated into groups, SVM+[7] can effectively use this structure information to improve generalization. Alternatively, we can view learning based on data from each group as an individual task, but all these tasks are somehow related; so the same problem can also be formulated as a multi-task learning problem. Following the SVM+ approach, we propose a new multi-task learning algorithm called svm+MTL, which can be thought as an adaptation of SVM+ for solving MTL problem. The connections between SVM+ and svm+MTL are discussed and their performance is compared using synthetic data sets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva2:2008:ijcnn,
author = "Renato R. da Silva and Claudio A. Policastro",
title = "An Enhancement of Relational Reinforcement Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0621.pdf},
url = {},
size = {},
abstract = {Relational reinforcement learning is a technique that combines reinforcement learning with relational learning or inductive logic programming. This technique offers greater expressive power than that one offered by traditional reinforcement learning. However, there are some problems when one wish to use it in a real time system. Most of recent research interests on incremental relational learning structure, that is a great challenge in this area. In this work, we are proposing an enhancement of TG algorithm and we illustrate the approach with a preliminary experiment. The algorithm was evaluated on a Blocks World simulator and the obtained results shown it is able to produce appropriate learn capability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(DalleMole:2008:ijcnn,
author = "Vilson L. DalleMole and Aluizio F. R. Araújo",
title = "The Growing Self-Organizing Surface Map",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0622.pdf},
url = {},
size = {},
abstract = {This paper presents a new Self-organizing Map suitable for recovering a 2D surface starting from points sampled on the object surface. Growing Self-organizing Surface Map (GSOSM), is a new algorithm of the growing SOM family that reproduce the surface as an incremental mesh composed of triangles which are approximately equilateral. GSOSM introduces a new connection learning rule, called Competitive Connection Hebbian Learning (CCHL), that produces a complete triangulation where CHL fails. Differently from other models such as Neural Meshes (NM), GSOSM recovers a surface topology from homogeneous samples distribution according to any presentation sequence. GSOSM map is a mesh that represents the object surface with a detail level established by a parameter, allowing different versions of a same object surface. Moreover, GSOSM reconstructions are very often meshes free of false or overlapping faces, and then GSOSM is a potential tool for virtual reconstruction of real objects. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(DeLooze:2008:ijcnn,
author = "Lori L. DeLooze ",
title = "Eclectic Method for Feature Reduction Using Self-Organizing Maps",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0625.pdf},
url = {},
size = {},
abstract = {This paper presents an eclectic method for extracting simple classification rules using a combination of a genetic algorithm, a Self-Organizing Map and the ID3 decision tree algorithm. After outlining the method for extracting rules, we assess them for effectiveness, complexity and precision and compare them with similar methods which use Support Vector Machines. While it is no surprise that the method proposed reduced the complexity of classification, it was surprising that the simple rules extracted from the SOMs were both more effective and more precise than the SOM from which they were extracted. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kamimura:2008:ijcnn,
author = "Ryotaro Kamimura ",
title = "Conditional Information and Information Loss for Flexible Feature Extraction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0627.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new informationtheoretic approach to competitive learning and self-organizing maps. We use several information-theoretic measures such as conditional information and information losses to extract main features in input patterns. First, conditional information content is used to show how much information is contained in a competitive unit or an input pattern. Then, information content in each variable is detected by information losses. The information loss is defined by difference between information with all input units and information without an input unit. We applied the method to an artificial data, the Iris problem, a student survey, a CPU classification problem and a company survey. In all cases, experimental results showed that main features in input patterns were clearly detected. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu9:2008:ijcnn,
author = "Xiaoxiang Liu and Henry Leung and George A. Lampropoulous",
title = "An Intelligent Through-the-Wall Recognition System for Homeland Security",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0628.pdf},
url = {},
size = {},
abstract = {The increasing demands for homeland security boost the development of an intelligent recognition system for through-the-wall sensing. A novel intelligent through-the-wall life recognition engine based on support vector machine (SVM) is provided herein. In this system, micro-Doppler signatures detected from through-the-wall radar are extracted and fed into a SVM classifier. Micro-Doppler effect has great potential for life recognition of human activities, nonhuman but vital subjects, and lifeless targets. Due to time-varying nonstationary characteristic of micro-Doppler feature and its high dimensionality, the SVM classifier is found effective in achieving both computation efficiency and accuracy for this application. Simulation results show that high classification performance is achieved using the proposed recognition system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Soltic:2008:ijcnn,
author = "S. Soltic and S. G. Wysoski and N. K. Kasabov",
title = "Evolving Spiking Neural Networks for Taste Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0629.pdf},
url = {},
size = {},
abstract = {The paper investigates the use of the spiking neural networks for taste recognition in a simple artificial gustatory model. We present an approach based on simple integrate-and-fire neurons with rank order coded inputs where the network is built by an evolving learning algorithm. Further, we investigate how the information encoding in a population of neurons influences the performance of the networks. The approach is tested on two real-world datasets where the effectiveness of the population coding and network's adaptive properties are explored. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Watanabe2:2008:ijcnn,
author = "Sumio Watanabe ",
title = "A Formula of Equations of States in Singular Learning Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0630.pdf},
url = {},
size = {},
abstract = {Almost all learning machines used in computational intelligence are not regular but singular statistical models, because they are nonidentifiable and their Fisher information matrices are singular. In singular learning machines, neither the Bayes a posteriori distribution converges to the normal distribution nor the maximum likelihood estimator satisfies the asymptotic normality, resulting that it has been difficult to estimate generalization performances. In this paper, we establish a formula of equations of states which holds among Bayes and Gibbs generalization and training errors, and show that two generalization errors can be estimated from two training errors. The equations of states proved in this paper hold for any true distribution, any learning machine, and a priori distribution, and any singularities, hence they define widely applicable information criteria. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yuan2:2008:ijcnn,
author = "Changsong Yuan and Xiangyang Zhu and Guangquan Liu and Min Lei ",
title = "Classification of the Surface EMG Signal Using RQA Based Representations",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0631.pdf},
url = {},
size = {},
abstract = {Feature extraction is a key element of pattern recognition for myoelectric control. In this paper, recurrence plots and recurrence quantification analysis (RQA) are used as the feature extractor for surface EMG signals. For eight different hand motions, two-channel EMG signals are recorded. Ten individual RQA parameters are calculated for each channel of EMG signals. With different combinations of individual RQA parameters, a set of feature vectors with dimensions varying from 2 to 20 are obtained. The feature vectors are used as the input to a BP neural network for motion classification. Experimental results show that with appropriate selections of feature vectors, the motion classification algorithm achieves desirable accurate rate. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Siek:2008:ijcnn,
author = "Michael Siek and Dimitri Solomatine",
title = "Multivariate Chaotic Models vs Neural Networks in Predicting Storm Surge Dynamics",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0632.pdf},
url = {},
size = {},
abstract = {The recently developed methods in nonlinear dynamics and chaos time series analysis are used in this study to analyze, delineate and quantify the underlying coastal water level and surge dynamics in the North Sea along several locations at the Dutch coast. This study analyzes seven water level and surge data sets, five of which characterize coastal locations and two relate to the open sea locations. Both the water level data and the surge data (with the astronomical tide removed) are analyzed. The main objective of this analysis is to delineate and quantify the underlying dynamics of the coastal water levels and to quantify the variability and predictability of the coastal dynamics along the Dutch coast based on time series of observables. Based on the reconstructed multivariate phase space of the water level and surge dynamics, adaptive multivariate local models were built which typically yield more reliable and accurate short-term predictions compared to neural networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Akhand:2008:ijcnn,
author = "M. A. H. Akhand and Md. Monirul Islam and Kazuyuki Murase",
title = "Training of Neural Network Ensemble Through Progressive Interaction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0633.pdf},
url = {},
size = {},
abstract = {This paper presents an interactive training method for neural network ensembles (NNEs). For an NNE, proposed method trains component neural networks (NNs) one after another sequentially and interactions among the NNs are maintained indirectly via an intermediate space, called information center (IC). IC manages outputs of all previously trained NNs. Update rule, to train an NN in conjunction with IC, is developed from negative correlation learning (NCL) and defined the proposed method as progressive NCL (pNCL). The introduction of such an information center in ensemble methods reduces the training time interaction among component NNs. The effectiveness of the proposed method is evaluated on several benchmark classification problems. The experimental results show that the proposed approach can improve the performance of NNEs. pNCL is incorporated with two popular NNE methods, bagging and boosting. It is also found that the performance of bagging and boosting algorithms can be further improved by incorporating pNCL with their training processes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu10:2008:ijcnn,
author = "Song Liu and Jagath C. Rajapakse",
title = "Protein Localization on Cellular Images with Markov Random Fields",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0634.pdf},
url = {},
size = {},
abstract = {There has been an increasing interest recently in identifying subcellular proteins from cellular images in order to understand subcellular activities of cells. However, accuracies of the prediction tend to decrease with the number of protein subcellular localization classes. Therefore in this paper, we introduce a multiple-cell model with a higher-order Markov random fields (MRF) to combine predictions on multiple cells to make inferences on protein localizations of individual cells. The proposed method showed a significant improvement in discrimination of protein subcellular localization patterns over the predictions by single cells. We also introduce structure learning of MRF, which indeed enhanced the predictions especially when the number of cells in the model becomes large. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sri:2008:ijcnn,
author = "Kavuri Swathi Sri and Jagath C. Rajapakse",
title = "Extracting EEG Rhythms Using ICA-R",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0635.pdf},
url = {},
size = {},
abstract = {Extracting brain rhythms from EEG signals has many applications including Brain Computer Interfacing. Here, we demonstrate how ICA with Reference (ICA-R) is used to extract brain rhythms, using appropriate reference signals. In particular, we evaluate four criteria for generating reference signals to use with ICA-R. We demonstrate the performance of these techniques in extracting μ and β rhythms from two real EEG datasets. The results indicate that ICA-R can be effectively used for extracting brain rhythms. The blind source separation technique decomposing autocorrelation for extracting reference signals outperformed other methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Heerden:2008:ijcnn,
author = "Willem S. van Heerden and Andries P. Engelbrecht",
title = "A Comparison of Map Neuron Labeling Approaches for Unsupervised
Self-Organizing Feature Maps",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0636.pdf},
url = {},
size = {},
abstract = {The self-organizing map (SOM) is an unsupervised neural network approach that reduces a high-dimensional data set to a representative and compact two-dimensional grid. In so doing, a SOM reveals emergent clusters within the data. Research has shown that SOMs lend themselves to visual and computational analysis for exploratory and data mining purposes. However, an important requirement for many SOM interpretations is the characterization of the map's emergent clusters. This process is often addressed by either a manual or automated map neuron labeling approach. This paper discusses techniques for the labeling of the unsupervised, supervised and semi-supervised variants of the SOM, and proposes some new methods. It also presents empirical results characterizing the performance of two automated labeling approaches for fully unsupervised SOMs when applied for example classification of experimental data sets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang6:2008:ijcnn,
author = "Cheng-San Yang and Li-Yeh Chuang and Jung-Chike Li and Cheng-Hong Yang",
title = "A Novel BPSO Approach for Gene Selection and Classification of Microarray Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0637.pdf},
url = {},
size = {},
abstract = {Selecting relevant genes from microarray data poses a huge challenge due to the high-dimensionality of the features, multi-class categories and a relatively small sample size. The main task of the classification process is to decrease the microarray data dimensionality. In order to analyze microarray data, an optimal subset of features (genes) which adequately represents the original set of features has to be found. In this study, we used a novel binary particle swarm optimization (NBPSO) algorithm to perform microarray data selection and classification. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The experimental results showed that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qiao:2008:ijcnn,
author = "Yuanhua Qiao and Jun Miao and Lijuan Duan and Yunfeng Lu",
title = "Image Segmentation Using Dynamic Mechanism Based PCNN Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0638.pdf},
url = {},
size = {},
abstract = {Pulse-coupled neuron networks (PCNN) can be efficiently applied to image segmentation. However, the performance of segmentation depends on the suitable PCNN parameters, which are obtained by manual experiment, and the effect of the segmentation needs to be improved for images with noise. In this paper, dynamic mechanism based PCNN(DMPCNN) is brought forward to simulate the integrate-and-fire mechanism, and it is applied to segment images with noise effectively. Parameter selection is based on dynamic mechanism. Experimental results for image segmentation show its validity and robustness. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pattaraintakorn:2008:ijcnn,
author = "Puntip Pattaraintakorn ",
title = "Analysis of Distributed Databases with a Hybrid Rough Sets Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0639.pdf},
url = {},
size = {},
abstract = {The aim of this paper is to offer mathematical proofs of Pawlak's rough set theory about distributed knowledge based on rough sets and relational databases. A case study on actual self-reported geriatric data for survival analysis is presented to provide a computational evidence of the distributed knowledge. Risk factors, prolongation time prediction rules and validation are also computed and discussed. We illustrate that dividing a decision table (or database) into smaller units will in general result in the loss of some information by rough set theory. },
keywords = {: Rough set theory, relational database, distributed knowledge, survival analysis, artificial intelligence.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Argha:2008:ijcnn,
author = "Ahmadreza Argha and Paknoosh Karimaghaee and Mehdi Roopaei",
title = "Iterative Learning Control for 2-D Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0642.pdf},
url = {},
size = {},
abstract = {In this paper, the application of iterative learning control (ILC) in two-dimensional systems is considered and a method of ILC for 2-D systems is introduced so that the output of the process follows a desired trajectory. In this method the input of process in each iteration is determined by an innovative method called two-dimensional method by means of the obtained error between the output of the process and the desired trajectory which was given in previous iteration and the ability of this new method is illustrated by computerized simulation and the obtained results are compared with the results of one-dimensional method which was adapted to a 2-D one. Also the convergence of these methods is considered. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Saeed:2008:ijcnn,
author = "Mehreen Saeed and Haroon Babri",
title = "Classifiers Based on Bernoulli Mixture Models for Text Mining and Handwriting Recognition Tasks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0643.pdf},
url = {},
size = {},
abstract = {In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have applied this method to two different types of datasets, i.e., one from the text mining domain and one from the handwriting recognition area. Empirical experiments demonstrate that we can obtain up to 99.9percent reduction in the dimensionality of the original feature set for sparse binary features. Classification accuracy also increases considerably when the combined model is used. This paper compares the performance of different classification algorithms when used in conjunction with the new feature set generated by Bernoulli mixtures. Using this hybrid model of learning we have achieved one of the best accuracy rates on the NOVA and GINA datasets of the 'agnostic vs. prior knowledge' competition held by the International Joint Conference on Neural Networks in 2007. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Esaki:2008:ijcnn,
author = "Tomohito Esaki and Tomonori Hashiyama",
title = "Extracting Human Players' Shogi Game Strategies from Game Records Using Growing SOM",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0644.pdf},
url = {},
size = {},
abstract = {Shogi game is similar to Chess and very popular in Japan. Computer Shogi programs are still in developing to defeat the professional human players. One of the main problems exists in estimating the circumstances of the game phases. It is said that there are three phases in the Shogi game, so called, opening, middle and endgame phase. The appropriate strategy to be selected differs depending on the proceeding phase. The professional human players classify states of the game phases properly. In this paper, we have carried out some experiments to extract human players' strategies on shogi game from game records using growing SOM. The results show the promising feature of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu7:2008:ijcnn,
author = "Xin Xu and Hongyu Zhang and Bin Dai and Han-gen He ",
title = "Self-Learning Path-Tracking Control of Autonomous Vehicles Using Kernel-Based Approximate Dynamic Programming",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0645.pdf},
url = {},
size = {},
abstract = {With the fast development of robotics and intelligent vehicles, there has been much research work on modeling and motion control of autonomous vehicles. However, due to model complexity, and unknown disturbances from dynamic environment, the motion control of autonomous vehicles is still a difficult problem. In this paper, a novel self-learning path-tracking control method is proposed for a car-like robotic vehicle, where kernel-based approximate dynamic programming (ADP) is used to optimize the controller performance with little prior knowledge on vehicle dynamics. The kernel-based ADP method is a recently developed reinforcement learning algorithm called kernel least-squares policy iteration (KLSPI), which uses kernel methods with automatic feature selection in policy evaluation to get better generalization performance and learning efficiency. By using KLSPI, the lateral control performance of the robotic vehicle can be optimized in a self-learning and data-driven style. Compared with previous learning control methods, the proposed method has advantages in learning efficiency and automatic feature selection. Simulation results show that the proposed method can obtain an optimized path-tracking control policy only in a few iterations, which will be very practical for real applications. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Attard:2008:ijcnn,
author = "Conrad Attard and Andreas A. Albrecht",
title = "On Axon Delay Functions and Spiking Activity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0646.pdf},
url = {},
size = {},
abstract = {Over the past few years, the importance of axonal conduction delays has been emphasized by a number of authors. Different models are proposed for the approximation of signal delays, where some of them have been evaluated in the context of the optimal neuronal layout problem. Our paper presents computational experiments on the impact of two wiring cost functions, proposed by Chklovskii and Shefi et al., when applied to interneuronal connections in small ML neuronal networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Haranadh:2008:ijcnn,
author = "G. Haranadh and C. Chandra Sekhar",
title = "Hyperparameters of Gaussian Process as Features for Trajectory Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0648.pdf},
url = {},
size = {},
abstract = {In this paper, we address the trajectory classification problem in Gaussian process framework without using Gaussian process based classification directly. Properties of the function corresponding to a trajectory are captured into the hyperparameters of a Gaussian process. As different trajectories have different properties, hyperparameters are different for these trajectories. In the hyperparametric space, different clusters are formed for noisy, shifted versions of the trajectories. The hyperparameters are used as features representing a trajectory and the classification task is performed in the hyperparametric space. Classification performance of the proposed method is evaluated on simulated data and also on realworld time series data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alnajjar:2008:ijcnn,
author = "Fady Alnajjar and Kazuyuki Murase",
title = "Sensor-Fusion in Spiking Neural Network that Generates Autonomous Behavior in Real Mobile Robot",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0649.pdf},
url = {},
size = {},
abstract = {We here introduce a novel adaptive controller for autonomous mobile robot that binds N types of sensory information. For each sensory modality, sensory-motor connection is made by a three-layered spiking neural network (SNN). The synaptic weights in the model have the property of spike timing-dependent plasticity (STDP) and regulated by presynaptic modulation signal from the sensory neurons. Each synaptic weight is incrementally adapted depending upon the firing rate of the presynaptic modulation signal and that of the hidden-layer neuron(s). Information from different types of sensors are bound at the motor neurons. A real mobile robot Khepera with the SNN controller quickly adapted into an open environment and performed the desired task successfully. This approach could be applicable to a robot with inputs of various sensory modalities and various types of motor outputs. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alnajjar2:2008:ijcnn,
author = "Fady Alnajjar and Indra Bin Mohd Zin",
title = "A Spiking Neural Network with Dynamic Memory for a Real Autonomous Mobile Robot in Dynamic Environment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0651.pdf},
url = {},
size = {},
abstract = {This work concerns practical issues surrounding the application of learning and memory in a real mobile robot towards optimal navigation in dynamic environments. A novel control system that contains two-level units (low-level and high-level) is developed and trained in a physical mobile robot "e-Puck". In the low-level unit, the robot's task is to navigate in a various local environments, by training N numbers of Spiking Neural Networks (SNN) that have the property of spike time-dependent plasticity. All the trained SNNs are stored in a tree-type memory structure, which is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in new and previously trained environments. The memory is designed to hold memories of various lengths and has a simple searching mechanism. For controlling the memory size, forgetting and on-line dynamic clustering techniques are used. Experimental results have proved that the proposed model can provide a robot with learning and memorizing capabilities enable it to survive in complex and dynamic environments. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li8:2008:ijcnn,
author = "Yuanqing Li and Chuanchu Wang and Haihong Zhang and Cuntai Guan ",
title = "An EEG-Based BCI System for 2D Cursor Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0654.pdf},
url = {},
size = {},
abstract = {In this paper, an electroencephalogram (EEG)- based brain computer interface (BCI) is proposed for two dimensional cursor control. The horizontal and vertical movements of the cursor are controlled by mu/beta rhythm and P300 potential respectively. The main advantages of this system are: (i) two almost independent control signals are produced simultaneously; (ii) the cursor can be moved from a random position to another random position in a screen. These advantages have been demonstrated in our experiment and data analysis. },
keywords = {: Brain-computer interface (BCI), electroencephalogram (EEG), cursor control, mu/beta rhythm, P300 potential.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lian:2008:ijcnn,
author = "Feng Lian and Chongzhao Han and Yong Shi",
title = "Adaptive On-Line Registration Algorithm Based on GLR",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0655.pdf},
url = {},
size = {},
abstract = {In practical system, the sensor biases may jump abruptly. An adaptive on-line algorithm is presented in this paper for this situation. The algorithm can detect the jump onset time and estimate the jump level base on General Likelihood Ratio (GLR) test. The Monte Carlo results show, our algorithm can adaptively estimate the bias jump level well and the estimation error will not increase remarkably as other previous registration algorithms. The bias estimation error also converges to the Cramer-Rao lower bound (CRLB) after the jumping. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang7:2008:ijcnn,
author = "Jian Yang and Xi Huang and Ying Tan and Xingui He ",
title = "Forecast of Driving Load of Hybrid Electric Vehicles by Using Discrete Cosine Transform and Support Vector Machine",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0656.pdf},
url = {},
size = {},
abstract = {As advances in green automotives, hybrid electric vehicle (HEV) has being given more and more attention in recent years. The power management control strategy of HEV is the key problem that determines the efficiency and pollution emission level of the HEV, which requires the forecast of driving load situation of HEV in advance. This paper proposes an efficient approach for forecasting the driving load of the HEV by using Discrete Cosine Transform (DCT) and Support Vector Machine (SVM). The DCT is used to extract features from raw data, and reduce the dimensionality of feature which will result in an efficient SVM classification. The SVM is used to classify the current driving load into one of five presetting levels of the driving load of the HEV. In such way, we can predict the driving load efficiently and accurately, which leads to a reasonable control to the HEV and gives as a high efficiency and low emission level as possible. Finally, a number of experiments are conducted to verify the validity of our proposed approach. Compared to current methods, our proposed approach gives a considerably promising performance through extensive experiments and comparison tests. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Almeida:2008:ijcnn,
author = "Leandro M. Almeida and Teresa Ludermir",
title = "An Improved Method for Automatically Searching Near-Optimal Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0657.pdf},
url = {},
size = {},
abstract = {This paper describes an improved version of a method that automatically searches near-optimal Multilayer feedforward Artificial Neural Networks using Genetic Algorithms. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can produce compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang9:2008:ijcnn,
author = "Wenjia Wang ",
title = "Some Fundamental Issues in Ensemble Methods",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0658.pdf},
url = {},
size = {},
abstract = {The ensemble paradigm for machine learning has been studied for more than two decades and many methods, techniques and algorithms have been developed, and increasingly used in various applications. Nevertheless, there are still some fundamental issues remaining to be addressed, and an important one is what factors affect the accuracy of an ensemble, and to what extent they do, which is thus taken as the main topic of this paper. The factors studied include the accuracy of individual models, the diversity among the individual models in an ensemble, decision-making strategy, and the number of the members used for constructing an ensemble. This paper firstly describes the conceptual and theoretical analyses on these factors, and then presents the possible relationships between them. The experiments have been conducted by using some benchmark data sets and some typical results are presented in the paper. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Savitha:2008:ijcnn,
author = "R. Savitha and S. Suresh and N. Sundararajan and P. Saratchandran",
title = "Complex-Valued Function Approximation Using an Improved BP Learning Algorithm for Feed-Forward Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0659.pdf},
url = {},
size = {},
abstract = {In a fully complex-valued feed-forward network, the convergence of the complex-valued backpropagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is very important in telecommunication and medical imaging applications. In this paper, we propose an improved complex-valued back propagation algorithm using an exponential activation function and a logarithmic minimization criterion, which approximates both the magnitude and phase well. Performance of the proposed scheme is evaluated using the complex XOR problem and a synthetic complex-valued function approximation problem. Also, a comparative analysis on the convergence of the existing fully complex and split complex networks is presented. },
keywords = {:- Split complex network, fully complex-valued networks, multi-layer perceptron, complex-valued elementary transcendental functions and its derivatives.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Matsushita:2008:ijcnn,
author = "Haruna Matsushita and Yoshifumi Nishio",
title = "Batch-Learning Self-Organizing Map with False-Neighbor Degree Between Neurons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0660.pdf},
url = {},
size = {},
abstract = {This study proposes a Batch-Learning Self- Organising Map with False-Neighbor degree between neurons (called BL-FNSOM). False-Neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false neighbour degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BLFNSOM changes the neighbourhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM. },
keywords = {genetic algorithms, genetic programming},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guile:2008:ijcnn,
author = "Geoffrey R. Guile and Wenjia Wang",
title = "Relationship Between Depth of Decision Trees and Boosting Performance",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0661.pdf},
url = {},
size = {},
abstract = {We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic datasets. The results show that feature nonreplacement enables decision trees deeper than simple stumps to be used, thereby allowing feature interaction to be taken into account. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Haraguchi:2008:ijcnn,
author = "Taku Haraguchi and Haruna Matsushita and Yoshifumi Nishio",
title = "Lazy Self-Organizing Map and its Behaviors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0662.pdf},
url = {},
size = {},
abstract = {The Self-Organising Map (SOM) is a famous algorithm for the unsupervised learning and visualisation introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organising Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lin:2008:ijcnn,
author = "Han Lin and Liu Xuegong and Zhang Yanning",
title = "Interpretation of River Main-Flow from Remote Sensing Images: Studying on Dynamic Transmission Cross-Correlation Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0664.pdf},
url = {},
size = {},
abstract = {The main-flow is wandering for sedimentation in downstream channel of the Yellow River, which threatened security greatly for flood control in the lower. It is a very difficult issue to interpret the main-flow information using remote sensing image. In this paper, with the flow characteristics of direction and continuously similarity in river channel, a Dynamic Transmission Cross-Correlation (DTCC) algorithm was proposed and employed to try to extract the main-flow information based on Landsat TM images in wandering reaches of the lower Yellow River. The results show that the interpretation main-flow is coincident with the field observed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Torres-Sospedra:2008:ijcnn,
author = "Joaquín Torres-Sospedra and Carlos Hernandez-Espinosa and Mercedes Fernandez-Redondo",
title = "Researching on Combining Boosting Ensembles",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0667.pdf},
url = {},
size = {},
abstract = {As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. The two key factors to design an ensemble are how to train the individual networks and how to combine them to give a single output. Boosting is a well known methodology to build an ensemble. Some boosting methods use an specific combiner (Boosting Combiner) based on the accuracy of the network. Although the Boosting combiner provides good results on boosting ensembles, the simple combiner Output Average worked better in three new boosting methods we successfully proposed in previouses papers. In this paper, we study the performance of sixteen different combination methods for ensembles previously trained with Adaptive Boosting and Average Boosting in order to see which combiner fits better on these ensembles. Finally, the results show that the accuracy of the ensembles trained with these original boosting methods can be improved by using the appropriate alternative combiner. In fact, the Output average and the Weighted average on low/medium sized ensembles provide the best results in most of the cases. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pan2:2008:ijcnn,
author = "Yunpeng Pan and Jun Wang",
title = "Nonlinear Model Predictive Control Using a Recurrent Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0671.pdf},
url = {},
size = {},
abstract = {As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed network is globally convergent to the optimal solution of the NMPC problem. Simulation results are presented to show the effectiveness and performance of the neural network approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva3:2008:ijcnn,
author = "João M. M. Silva and Eugenius Kaszkurewicz",
title = "An LMI-Neural Network Based Solution to the Load Balancing Problem for Heterogeneous Local Clusters",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0672.pdf},
url = {},
size = {},
abstract = {A solution for the load balancing problem in local clusters of heterogeneous processors is proposed within the setting of delayed artificial neural networks, optimal control and Linear Matrix Inequalities (LMI) theory. Based on a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of Delayed Cellular Neural Networks model. A systematic method of controller synthesis is derived, based on two coupled Linear Matrix Inequalities — one guaranteeing global convergence and the other guaranteeing performance in the linear region of operation. Simulations and computational experiments show the efficiency of this approach, reducing load balancing time. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cai:2008:ijcnn,
author = "Song Cai and William W. Hsieh and Alex J. Cannon",
title = "A Comparison of Bayesian and Conditional Density Models in Probabilistic Ozone Forecasting",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0674.pdf},
url = {},
size = {},
abstract = {Probabilistic models were developed to provide predictive distributions of daily maximum surface level ozone concentrations. Five forecast models were compared at two stations (Chilliwack and Surrey) in the Lower Fraser Valley of British Columbia, Canada, with local meteorological variables used as predictors. The models were of two types, conditional density models and Bayesian models. The Bayesian models (especially the Gaussian Processes) gave better forecasts for extreme events, namely poor air quality events defined as having ozone concentration ≥82 ppb. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Daneshyari:2008:ijcnn,
author = "Moayed Daneshyari ",
title = "A Neurochaotic PSO-Guided Network Based Upon Perturbed Duffing Oscillator",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0675.pdf},
url = {},
size = {},
abstract = {This paper introduces a neurochaotic information processor based upon perturbed Duffing equation. The proposed chaotic neural network has parameters to tune by which decision is made to behave either chaotically or periodically. The neurochaotic nonlinear network adopts the chaotic dynamics of so-called Duffing oscillator for the chaotic movement in the search space. It then uses the benefits of fast convergence of particle swarm optimization to settle down into the attractors of periodic solutions. },
keywords = {: Chaos, neural network, particle swarm optimization, pattern recognition, Duffing oscillator.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kurogi:2008:ijcnn,
author = "Shuichi Kurogi and Yohei Koshiyama",
title = "Model Switching Predictive Control Using Bagging CAN2 and
First-Difference Signals for Temperature Control of RCA Cleaning Solutions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0676.pdf},
url = {},
size = {},
abstract = {The RCA cleaning method is the industry standard way to clean silicon wafers, where temperature control is important for a stable cleaning performance. However, it is difficult mainly because the RCA solutions cause nonlinear and time-varying exothermic chemical reactions. So far, the MSPC (model switching predictive controller) using the CAN2 (competitive associative net 2) has been developed and the effectiveness has been validated. However, we have observed that the control performance, such as the settling time and the overshoot, does not always improve with the increase of the number of learning iterations of the CAN2. To solve this problem, we introduce the bagging method for the CAN2 and first-difference signals for the MSPC. The effectiveness of the present method is shown by means of computer simulation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kurogi2:2008:ijcnn,
author = "Shuichi Kurogi and Daisuke Wakeyama and Hideaki Koya and Shota Okada",
title = "Application of CAN2 to Plane Extraction from 3D Range Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0677.pdf},
url = {},
size = {},
abstract = {An application of CAN2 (competitive associative net 2) to plane extraction from 3D range images obtained by a LRF (laser range finder) is presented. The CAN2 basically is a neural net which learns efficient piecewise linear approximation of nonlinear functions, and in this application it is used for learning piecewise planner surfaces from the range image. As a result of the learning, the obtained piecewise planner surfaces are much smaller and much more than the actual planner surfaces, so that we introduce a method to gather piecewise planner surfaces for reconstructing the actual planner surfaces. We apply this method to real range images, and examine the performance and the comparative advantage to other methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chiang:2008:ijcnn,
author = "Ching-Tsan Chiang and Yu-Bin Lin",
title = "The Learning Convergence of High Dimension CMAC_GBF",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0678.pdf},
url = {},
size = {},
abstract = {High-Dimension Cerebellar Model Articulation Controller with General Basis Function (CMAC_GBF [2]) is developed and its learning convergence is also proved in this study. Up till now, the applications of CMAC are mainly used as controller or system identification (function mapping). Due to the guaranteed convergence and learning speed of CMAC, all the applications have shown good performance. But for high-dimensional mapping or control, it requires a lot of memories; the consequence is not able to use CMAC_GBF or to use enormous resources to complete its mission. When CMAC_GBF is employed, the necessary memory is growing exponentially with increasing input dimensions, and this slows down the learning speed or turns out to be impossible. In this project, S_CMAC_GBF [4] (A simple structure for CMAC_GBF) is employed to realize high-dimension application ability. Two 6-input nonlinear systems are employed to demonstrate the learning performance and the required practical memories of S_CMAC_GBF in high-dimensional applications. Briefly, the learning convergence is also proved. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li9:2008:ijcnn,
author = "XuQin Li and Carlos Ramirez and Evor L. Hines and Mark S. Leeson and Phil Purnell and Mark Pharaoh",
title = "Pattern Recognition of Fiber-Reinforced Plastic Failure Mechanism Using Computational Intelligence Techniques",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0679.pdf},
url = {},
size = {},
abstract = {Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cai2:2008:ijcnn,
author = "Chenghui Cai and Silvia Ferrari",
title = "A Q-Learning Approach to Developing an Automated Neural Computer Player for the Board Game of CLUE®",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0682.pdf},
url = {},
size = {},
abstract = {The detective board game of CLUE® can be viewed as a benchmark example of the treasure hunt problem, in which a sensor path is planned based on the expected value of information gathered from targets along the path. The sensor is viewed as an information gathering agent that makes imperfect measurements or observations from the targets, and uses them to infer one or more hidden variables (such as, target features or classification). The treasure hunt problem arises in many modern surveillance systems, such as demining and reconnaissance robotic sensors. Also, it arises in the board game of CLUE®, where pawns must visit the rooms of a mansion to gather information from which the hidden cards can be inferred. In this paper, Q-Learning is used to develop an automated neural computer player that plans the path of its pawn, makes suggestions about the hidden cards, and infers the answer, often winning the game. A neural network is trained to approximate the decision-value function representing the value of information, for which there exists no general closed-form representation. Bayesian inference, test (suggestions), and action (motion) decision making are unified using an MDP framework. The resulting computer player is shown to outperform other computer players implementing Bayesian networks, or constraint satisfaction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Muro:2008:ijcnn,
author = "Gianluca Di Muro and Silvia Ferrari",
title = "A Constrained-Optimization Approach to Training Neural Networks for Smooth Function Approximation and System Identification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0683.pdf},
url = {},
size = {},
abstract = {A constrained-backpropagation training technique is presented to suppress interference and preserve prior knowledge in sigmoidal neural networks, while new information is learned incrementally. The technique is based on constrained optimization, and minimizes an error function subject to a set of equality constraints derived via an algebraic training approach. As a result, sigmoidal neural networks with long term procedural memory (also known as implicit knowledge) can be obtained and trained repeatedly on line, without experiencing interference. The generality and effectiveness of this approach is demonstrated through three applications, namely, function approximation, solution of differential equations, and system identification. The results show that the long term memory is maintained virtually intact, and may lead to computational savings because the implicit knowledge provides a lasting performance baseline for the neural network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang8:2008:ijcnn,
author = "Jufeng Yang and Guangshun Shi and Qingren Wang and Yong Zhang",
title = "Recognition of On-line Handwritten Chemical Expressions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0686.pdf},
url = {},
size = {},
abstract = {In this paper, we study the major modules of on-line handwritten chemical expressions recognition. We propose a novel algorithm that combines two separate methods to segment expressions, one of which is based on structural information and the other on partial recognition. The algorithm improves the traditional algorithm at the stage of recognition, which consists of a substance recognizer and a character recognizer. To meet the demand of actual applications, the paper also designs a standard feature set to deal with the related issues and presents a flexible process of human-computer interaction to help users modify the recognition result. The experimental results show that the presented algorithm is reliable. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li10:2008:ijcnn,
author = "Jinbo Li and Shiliang Sun",
title = "Energy Feature Extraction of EEG Signals and a Case Study",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0687.pdf},
url = {},
size = {},
abstract = {Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered as a feature extractor. The solution which optimizes the EED criterion is presented in this paper and according to experimental results, EED is a promising method for extracting energy features in EEG signal classification. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu11:2008:ijcnn,
author = "Panzhi Liu and Chongzhao Han and Jing Jie",
title = "A Threshold Factor Approach Method for CFAR Detector Based on Stochastic Particle Swarm Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0691.pdf},
url = {},
size = {},
abstract = {Based on the perfect properties of stochastic particle swarm optimization (SPSO), such as the property of robust and quick convergence, a new scheme is applied to estimate scaling factor for radar constant false alarm rate (CFAR) detectors. Owing to few constraints, it can estimate scaling factor for single radar as well as radar netting system. The numerical results indicate that the particle swarm optimizer has been found to be accuracy and fast in searching the threshold factor T of CFAR detector under any designed probability of false alarm. },
keywords = { CFAR detector, Scaling factor, and stochastic particle swarm optimization (SPSO)},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lee2:2008:ijcnn,
author = "S. L. A. Lee and A. Z. Kouzani and E. J. Hu",
title = "From Lung Images to Lung Models: A Review",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0693.pdf},
url = {},
size = {},
abstract = {Automated 3D lung modeling involves analyzing 2D lung images and reconstructing a realistic 3D model of the lung. This paper presents a review of the existing works on automatic formation of 3D lung models from 2D lung images. A common framework for 3D lung modeling is proposed. It consists of eight components: image acquisition, image preprocessing, image segmentation, boundary creation, image recognition, image registration, 3D surface reconstruction, and 3D rendering and visualization. The algorithms used by the existing systems to implement these components are also reviewed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Notsu:2008:ijcnn,
author = "Akira Notsu and Hidetomo Ichihashi and Katsuhiro Honda ",
title = "State and Action Space Segmentation Algorithm in Q-Learning ",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0694.pdf},
url = {},
size = {},
abstract = {In this Paper, we propose a novel Q-learning algorithm that segmentalizes the agent environment and action. This algorithm is learned through interation with an environment and action. This algorithm is learned through interaction with an environment and provides deterministic space segmentation. The purposes of this study can be divided into two main groups: search domain reduction and heuristic space segmentation. In our method, the most activated space segment is divided into new two segments with the learning by a heuristic and recognizable method. Appropriate search domain reduction can minimize the learning time and enables us to recognize the evolutionary process. This segmentation method is also designed for social simulation models. Social space segmentation, such as language systems and culture, is revealed by multi-agent social simulation with our method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ang:2008:ijcnn,
author = "Kai Keng Ang and Zheng Yang Chin and Haihong Zhang and Cuntai Guan",
title = "Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0695.pdf},
url = {},
size = {},
abstract = {In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel Filter Bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporalspatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher crossvalidation accuracies compared to prevailing approaches. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Iwamura:2008:ijcnn,
author = "Kazuki Iwamura and Shigeo Abe",
title = "Sparse Support Vector Machines Trained in the Reduced Empirical Feature Space",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0699.pdf},
url = {},
size = {},
abstract = {We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky factorization of the kernel matrix, and train the SVM in the dual form in the reduced empirical feature space. Since the mapped linearly independent training data span the empirical feature space, the linearly independent training data become support vectors. Thus if the number of linearly independent data is smaller than the number of support vectors trained in the feature space, sparsity is increased. By computer experiments we show that in most cases we can reduce the number of support vectors without deteriorating the generalization ability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tan4:2008:ijcnn,
author = "Chue Poh Tan and Chen Change Loy and Weng Kin Lai and Chee Peng Lim",
title = "Robust Modular Artmap For Multi-Class Shape Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0700.pdf},
url = {},
size = {},
abstract = {This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAOSVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Junyao:2008:ijcnn,
author = "Gao Junyao and Gao Xueshan and Zhu Wei and Zhu Jianguo and Wei Boyu",
title = "Fault-Tolerant and High Reliability Space Robot Design and Research",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0701.pdf},
url = {},
size = {},
abstract = {Space robot reliability is a fatal important problem. Any fault and error may damage spacecraft. This paper suggests a space robot system with two alternate drive system, two alternate control system, redundant freedom, two alternate communicate system. The space robot is a fault-tolerant system. Fault tree is used to analysis space robot. Any one element on the space robot fault doesn't influence robot work. Space robot reliability is raised to a high level through this design. This design scheme is a practice scheme for space robot. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alnajjar3:2008:ijcnn,
author = "Fady Alnajjar and Abdul Rahman Hafiz",
title = "Vision-Sensorimotor Abstraction and Imagination Towards Exploring Robot's Inner World",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0702.pdf},
url = {},
size = {},
abstract = {Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing them with its internal simulation. The accuracy of this hypothesis is strongly based on the agent's experiences. As much as the agent knows about the environment, as much as it can build a strong internal representation about it. Although many works have been presented regarding to this hypothesis with various levels of success. At the sensorimotor abstraction level, where extracting data from the environment occur, however, none of them have so far used the robot's vision as a sensory input. In this study, vision-sensorimotor abstraction is presented through memory-based learning in a real mobile robot "Hemisson" to investigate the possibilities of explaining its inner world based on internal simulation of perception and action at the abstract level. The analysis of the experiments illustrate that our robot with vision sensory input has developed some kind of simple associations or anticipation mechanism through interacting with the environment, which enables, based on its history and the present situation, to guide its behavior in the absence of any external interaction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang8:2008:ijcnn,
author = "W. Zhang and B. Li and W. Zhou",
title = "A LLE-Based Approach to Sensor Fault Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0703.pdf},
url = {},
size = {},
abstract = {Feature extraction has been widely used in sensor fault detection. Commonly used feature extraction methods such as PCA and MDS involve signal process of liner time-invariant systems, which are less effective in dealing with the nonlinear systems. In this paper, we will present that Local Linear Embedding (LLE) concept is adopted to solve the fault detection problems and that certain enhancement have been made to make LLE approach more efficient and robust in the extraction of signal features. Test results are given to demonstrate the effectiveness of the enhanced LLE method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ahmad:2008:ijcnn,
author = "Shandar Ahmad and Zulfiqar Ahmad",
title = "ATP-Binding Site as a Further Application of Neural Networks to Residue Level Prediction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0704.pdf},
url = {},
size = {},
abstract = {Similar neural network models based on single sequence and evolutionary profiles of residues have been successfully used in the past for predicting secondary structure, solvent accessibility, protein-, DNA- and carbohydrate- binding sites. ATP is a ubiquitous ligand in all living-systems, involved in most biological functions requiring energy and charge transfer. Prediction of ATP-binding site from single sequences and their evolutionary profiles at a high throughput rate can be used at genomic level as well as quick clues for site-directed mutagenesis experiments. We have developed a method for such predictions to demonstrate yet another application of sequence-base prediction algorithms using neural networks. This method can achieve 81percent sensitivity and 69percent specificity which are mutually adjustable in a wide range on a three-fold cross-validation data set. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hu3:2008:ijcnn,
author = "Yanjie Hu and Juanjuan Pang",
title = "Financial Crisis Early-Warning Based on Support Vector Machine",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0705.pdf},
url = {},
size = {},
abstract = {Analyzing the principle of typical financial crisis early-warning model, this study summarizes the limitations of them and their requirement of variance. An empirical research is carried out on how to sample the Chinese listed companies of A-stock market in Shanghai and Shenzhen, and how to determine the core parameters of support vector machine (SVM) as well. This research also studies the predicting accuracy in 1-3 years and the performance on condition that some data are missing. At last the contrastive analysis is made between SVM model and the Logistic model. Our experimentation results demonstrate that SVM outperforms the Logistic model and SVM also has a sound accuracy under the data missing. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gao:2008:ijcnn,
author = "Changjian Gao and Mazad S. Zaveri and Dan Hammerstrom",
title = "CMOS/CMOL Architectures for Spiking Cortical Column",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0707.pdf},
url = {},
size = {},
abstract = {We present a spiking cortical column model based on neural associative memory, and demonstrate architectures for emulating the cortical column model with nanogrid molecular circuitry. We investigate a number of options for cost-effective hardware with digital CMOS and mixed-signal CMOL, a hybrid CMOS/nanogrid technology. We also give an example of a dynamic learning algorithm that is a suitable match to CMOL implementation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Junyao2:2008:ijcnn,
author = "Gao Junyao and Gao Xueshan and Zhu Wei and Zhu Jianguo and Wei Boyu",
title = "Light Mobile Robot's Weight Design and Research",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0708.pdf},
url = {},
size = {},
abstract = {Light mobile robot's weight design is a important problem which decides function and ability of mobile robot. In this paper, a total design method is advanced. There are many factors to consider, include mechanical part, electrical part, and task part. Each parts of robot must be carefully calculated and designed. There are many experiences in it. A light mobile robot is carefully analyzed as an example. The method can help mobile robot designers on how to design a robot's weight quickly and not waste time and money. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ishi:2008:ijcnn,
author = "Tsuneyoshi Ishi and Shigeo Abe",
title = "Feature Selection Based on Kernel Discriminant Analysis for Multi-Class Problems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0711.pdf},
url = {},
size = {},
abstract = {We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an n-class problem, which finds n-1 eigenvectors on which the projected class data are locally maximally separated. The proposed criterion is the sum of the objective function values of KDA associated with the n-1 eigenvectors. The criterion results in calculating the sum of n-1 eigenvalues associated with the eigenvectors and is shown to be monotonic for the deletion or addition of features. Using the backward feature selection strategy, for several multi-class data sets, we evaluated the proposed criterion and the criterion based on the recognition rate of the support vector machine (SVM) evaluated by cross-validation. From the standpoint of generalization ability the proposed criterion is comparable with the SVM-based recognition rate, although the proposed method does not use cross-validation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lu4:2008:ijcnn,
author = "Shijian Lu and Cuntai Guan and Haihong Zhang",
title = "Learning Adaptive Subject-Independent P300 Models for EEG-Based Brain-Computer Interfaces",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0712.pdf},
url = {},
size = {},
abstract = {This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10 subjects performing word spelling in an oddball paradigm. The results are very positive: the adapted models with unlabeled data yield virtually the same classification accuracy as the conventional methods with labeled data. Therefore, it proves the feasibility of P300-based BCIs which can be applied directly to a new subject without training sessions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu12:2008:ijcnn,
author = "Xiao-Hua Liu and Cheng-Lin Liu and Xinwen Hou",
title = "A Pooled Subspace Mixture Density Model for Pattern Classification in High-Dimensional Spaces",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0714.pdf},
url = {},
size = {},
abstract = {Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ``the curse of dimensionality''. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components of each class share a subspace and the complementary subspace is incorporated in the density function. The subspace and Gaussian mixture density are estimated simultaneously in EM iteration steps. We apply the density model to pattern classification in experiments on UCI datasets and compare the proposed method with previous ones. The experimental results demonstrate the superiority of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Torikai:2008:ijcnn,
author = "Hiroyuki Torikai and Sho Hashimoto ",
title = "A Hardware-Oriented Learning Algorithm for a Digital Spiking Neuron",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0715.pdf},
url = {},
size = {},
abstract = {The digital spiking neuron is a wired system of shift registers and behaves like a simplified neuron model. By adjusting the wirings among the registers, the neuron can generate various spike-trains. In this paper some basic relations between the wiring pattern and spike-train characteristics are analyzed. Based on the analysis results, a hardware-oriented learning algorithm is proposed. The learning algorithm and the digital neuron are implemented by a hardware description language (HDL). It is shown that the learning algorithm enables the digital neuron to approximate various spike-trains generated by an analog spiking neuron model. In addition, some basic experimental measurements are provided by using a field programmable gate array (FPGA). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Oh:2008:ijcnn,
author = "Jiyong Oh and Chong-Ho Choi and Chunghoon Kim",
title = "Kernel Discriminant Analysis Using Composite Vectors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0716.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new kernel discriminant analysis using composite vectors (C-KDA). We show that employing composite vectors is similar to using more samples by analysis, which is a great advantage in classification problems when the size of training samples is small. Motivated by this, we apply composite vectors to kernel-based methods, which may have overfitting problems when training samples are not sufficient. Experimental results using several data sets from UCI machine learning repository show that C-KDA gives a better performance compared to other methods based on primitive input variables and linear discriminant analysis using composite vectors (C-LDA) when the training sample size is relatively small. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pang:2008:ijcnn,
author = "Shaoning Pang and Nikola Kasabov",
title = "r-SVMT: Discovering the Knowledge of Association Rule Over SVM Classification Trees",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0719.pdf},
url = {},
size = {},
abstract = {This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure. The benefits of r-SVMT are that the decision-tree rule provides better comprehensibility, and the support-vector rule retains the good classification accuracy of SVM. Furthermore, the r-SVMT is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution, which profits from the super generalization ability of SVMT owing to the aggregation of a group of SVMs. Experiments with a gaussian synthetic data, seven benchmark cancers diagnosis have highlighted the utility of SVMT and r-SVMT on encoding and decoding rule knowledge, as well as the superior properties of r-SVMT as compared to a completely support-vector based rule extraction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Abe:2008:ijcnn,
author = "Tohru Abe and Toshimichi Saito",
title = "An Approach to Prediction of Spatio-Temporal Patterns Based on Binary Neural Networks and Cellular Automata",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0720.pdf},
url = {},
size = {},
abstract = {This Paper studies application of binary neural networks (BNN) to prediction for spatio-temporal patterns. In the approach, we assume that the objective spatio-temporal patterns can be approximated by a cellular automaton (CA). Teacher signals are extracted from a part of objective pattern and are used for learning of the BNN. The BNN is used to govern dynamics of CA that outputs prediction patterns. Performing basic numerical experiments, we have investigated relation among the number of teacher signals, the number of hidden neurons and prediction performance. The results provide basic information for development of robust prediction method for digital spatio-temporal patterns. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Amin:2008:ijcnn,
author = "Md. Faijul Amin and Md. Monirul Islam",
title = "Single-Layered Complex-Valued Neural Networks and Their Ensembles for Real-Valued Classification Problems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0722.pdf},
url = {},
size = {},
abstract = {This paper presents a complex-valued neuron (CVN) model for real-valued classification problems incorporating a new activation function. The activation function maps complex-valued net-inputs (sum of weighted inputs) of a neuron into bounded real-values, and its role is to divide the net-input space into different regions for different classes. A gradient-descent learning rule has been derived to train the CVN. Such a CVN is able to solve all possible twoinput Boolean functions. For further investigation, single layered complex-valued neural networks (i.e. without hidden units) are applied on the real-world multi-class classification problems. The results are comparable to the conventional multilayer real-valued neural networks. It is also shown that the performance can be improved further by using their ensembles. Negative correlation learning (NCL) algorithm has been used to create the ensembles. Since NCL is a gradientdescent based algorithm, the proposed activation function is well suited for it. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ono:2008:ijcnn,
author = "Aiko Ono and Shigeo Sato and Mitsunaga Kinjo and Koji Nakajima",
title = "Study on the Performance of Neuromorphic Adiabatic Quantum Computation Algorithms",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0725.pdf},
url = {},
size = {},
abstract = {Quantum computation algorithms indicate possibility that non-deterministic polynomial time (NP-time) problems can be solved much faster than by classical methods. Farhi et al. [2], [3] have proposed an adiabatic quantum computation (AQC) for solving the three-satisfiability problem (3-SAT). We have proposed a neuromorphic quantum computation algorithm based on AQC, in which an analogy to an artificial neural network (ANN) is considered in order to design a Hamiltonian. However, in the neuromorphic AQC, the relation between its computation time and the probability of correct answers is not clear yet. In this paper, we study both of residual energy and the probability of finding solution as a function of computation time. The results show that the performance of the neuromorphic AQC depends on the characteristic of Hamiltonians. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ferreira3:2008:ijcnn,
author = "Leonardo V. Ferreira and Eugenius Kaszkurewicz and Amit Bhaya",
title = "Image Restoration Using L1-Norm Regularization and a Gradient-Based Neural Network with Discontinuous Activation Functions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0726.pdf},
url = {},
size = {},
abstract = {The problem of restoring images degraded by linear position invariant distortions and noise is solved by means of a L1-norm regularization, which is equivalent to determining a L1- norm solution of an overdetermined system of linear equations, which results from a data-fitting term plus a regularization term that are both in L1 norm. This system is solved by means of a gradient-based neural network with a discontinuous activation function, which is ensured to converge to a L1-norm solution of the corresponding system of linear equations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Park:2008:ijcnn,
author = "Dong-Chul Park and Dong-Min Woo",
title = "Image Classification Using Gradient-Based Fuzzy c-Means with Divergence Measure",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0727.pdf},
url = {},
size = {},
abstract = {This paper proposes a novel classification method for image retrieval using Gradient-Based Fuzzy c-Means with Divergence Measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that uses the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification algorithm outperforms conventional algorithms such as the traditional Self-Organizing Map (SOM) and Fuzzy c-Means (FCM) by 27percent-28.5percent in terms of accuracy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lin2:2008:ijcnn,
author = "Minlong Lin and Ke Tang and Xin Yao",
title = "Selective Negative Correlation Learning Algorithm for Incremental Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0728.pdf},
url = {},
size = {},
abstract = {Negative correlation learning (NCL) is a successful scheme for constructing neural network ensembles. In batch learning mode, NCL outperforms many other ensemble learning approaches. Recently, NCL is also shown to be a potentially powerful approach to incremental learning, while the advantage of NCL has not yet been fully exploited. In this paper, we propose a selective NCL approach for incremental learning. In the proposed approach, the previously trained ensemble is cloned when a new data set presents and the cloned ensemble is trained on the new data set. Then, the new ensemble is combined with the previous ensemble and a selection process is applied to prune the whole ensemble to a fixedsize. Simulation results on several benchmark datasets show that the proposed algorithm outperforms two recent incremental learning algorithms based on NCL. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Matsuda:2008:ijcnn,
author = "Yoshitatsu Matsuda and Kazunori Yamaguchi",
title = "A Connection-limited Neural Network by Infomax and Infomin",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0733.pdf},
url = {},
size = {},
abstract = {It is well known that edge filters in the visual system can be generated by the InfoMax principle. But, such models are nonlinear and employ fully-connected network structures. In this paper, a new artificial network model is proposed, which is based on the ``InfoMin'' principle and linear multilayer ICA (LMICA). This network uses cumulantbased objective functions which are derived from the InfoMax and InfoMin principles with large noise. Because the objective functions do not rely on any nonlinear models, a linear model can be employed. It simplifies the model considerably. Besides, this network can deal with quite large number of neurons by employing a connection-limited structure as in LMICA. In addition, it is more efficient than even LMICA because it does not need any prewhitening. Numerical experiments show that this network generates hierarchical edge filters from large-size natural scenes and verify the validity of the InfoMin principle. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Petreska:2008:ijcnn,
author = "Biljana Petreska and Yossi Yovel",
title = "A Neural Model of Demyelination of the Mouse Spinal Cord",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0734.pdf},
url = {},
size = {},
abstract = {This paper presents a neural network model of demyelination of the mouse motor pathways, coupled to a central pattern generation (CPG) model for quadruped walking. Demyelination is the degradation of the myelin layer covering the axons which can be caused by several neurodegenerative autoimmune diseases such as multiple sclerosis. We use this model - to our knowledge first of its kind - to investigate the locomotion deficits that appear following demyelination of axons in the spinal cord. Our model meets several physiological and behavioral results and predicts that whereas locomotion can still occur at high percentages of demyelination damage, the distribution and location of the lesion are the most critical factors for the locomotor performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang6:2008:ijcnn,
author = "Kou-Yuan Huang and Liang-Chi Shen and Chun-Yu Chen",
title = "Higher Order Neural Networks for Well Log Data Inversion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0735.pdf},
url = {},
size = {},
abstract = {Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sollacher:2008:ijcnn,
author = "Rudolf Sollacher and Huaien Gao",
title = "Efficient Online Learning with Spiral Recurrent Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0736.pdf},
url = {},
size = {},
abstract = {Distributed intelligent systems like self-organizing wireless sensor and actuator networks are supposed to work mostly autonomous even under changing environmental conditions. This requires robust and efficient self-learning capabilities implementable on embedded systems with limited memory and computational power. We present a new solution called Spiral Recurrent Neural Networks with an online learning based on an extended Kalman filter and gradients as in Realtime Recurrent Learning. We illustrate its performance using artificial and reallife time series and compare it to other approaches. Finally we describe a few potential applications. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guile2:2008:ijcnn,
author = "Geoffrey R. Guile and Wenjia Wang",
title = "Boosting for Feature Selection for Microarray Data Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0739.pdf},
url = {},
size = {},
abstract = {We have investigated the use of boosting techniques for feature selection for microarray data analysis. We propose a novel algorithm for feature selection and have tested it on three datasets. The results clearly show that our boosting technique for feature selection outperformed the Wilcoxon-Mann-Whitney U-test commonly used in microarray data analysis, and produced more accurate boosting ensembles when they were constructed with the features selected by our technique. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Dhahri:2008:ijcnn,
author = "H. Dhahri and Adel. M. Alimi and F. Karray",
title = "Designing Beta Basis Function Neural Network for Optimization Using Particle Swarm Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0740.pdf},
url = {},
size = {},
abstract = {Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a suitable set of inputs and a suitable BBF network structure. Evolutionary methodologies offer the potential to automate all or part of these steps. This study illustrates how a hybrid BBFN-PSO system can be constructed, and applies the system to a number of datasets. The utility of the resulting BBFNs on these optimization problems is assessed and the results from the BBFN-PSO hybrids are shown to be competitive against the best performance on these datasets using alternative optimization methodologies. The results show that within these classes of evolutionary methods, particle swarm optimization algorithms are very robust, effective and highly efficient in solving the studied class of optimization problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mahdavi:2008:ijcnn,
author = "Nariman Mahdavi and Ali A.Gorji and Mohammad B. Menhaj and Saeedeh Barghinia",
title = "A Variable Structure Neural Network Model For Mid-Term Load Forecasting of Iran National Power System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0741.pdf},
url = {},
size = {},
abstract = {Mid-term load forecasting is taken into account as one of the most important policies in the electricity market and brings about many financial, commercial and, even, political benefits. In this paper, artificial neural networks are represented for mid-term load forecasting of Iran national power system. To do so, the multi layer perceptron (MLP) neural network as well as radial basis function (RBF) networks are considered as parametric structures. Moreover, because of some problems such as a limitation on the number of data for training networks, the number of neurons and basis functions is also adjusted during the training process. The obtained optimal networks are used to forecast the electricity pick load of the next 52 weeks. Simulation results show the superiority of both proposed structures in the mid-term load forecasting of Iran national power system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Karatzas:2008:ijcnn,
author = "Kostas D. Karatzas and George Papadourakis and Ioannis Kyriakidis",
title = "Understanding and Forecasting Atmospheric Quality Parameters with the Aid of ANNs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0743.pdf},
url = {},
size = {},
abstract = {A problem solving domain for the application of artificial intelligence (AI) methods towards knowledge discovery for the purposes of modelling and forecasting is urban air quality. This domain has the specific characteristic that the key parameters of interest (pollutant concentration criteria) have multiple temporal (and spatial) scales. The present paper applies ANNs for the operational forecasting of the 8-hour running average for Ozone, 24 hours in advance, for two locations in Athens, Greece. Results verify the ability of the methods to analyze and model this knowledge domain and to forecast the levels of key parameters that provide direct input to the environmental decision making process. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Schneegass:2008:ijcnn,
author = "Daniel Schneegass and Steffen Udluft and Thomas Martinetz",
title = "Uncertainty Propagation for Quality Assurance in Reinforcement Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0744.pdf},
url = {},
size = {},
abstract = {In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking into account the derived Q-function's uncertainty, which stems from the uncertainty of the estimators used for the MDP's transition probabilities and the reward function. We apply uncertainty propagation parallelly to the Bellman iteration and achieve confidence intervals for the Q-function. In a second step we change the Bellman operator as to achieve a policy guaranteeing the highest minimum performance with a given probability. We demonstrate the functionality of our method on artificial examples and show that, for an important problem class even an enhancement of the expected performance can be obtained. Finally we verify this observation on an application to gas turbine control. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Fu3:2008:ijcnn,
author = "Siyao Fu and Qi Zuo and Zeng-Guang Hou and Zize Liang and Min Tan and Fengshui Jing and Xiaoling Fu",
title = "Unsupervised Learning of Categories from Sets of Partially Matching Image Features for Power Line Inspection Robot",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0745.pdf},
url = {},
size = {},
abstract = {Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications in practice. For this purpose, we present an computationally efficient approach that does not need supervision and is capable of learning object categories automatically from unlabeled images which are represented by an set of local features, and all sets are clustered according to their partial-match feature correspondences, which is done by a enhanced Spatial Pyramid Match algorithm (E-SPK). Then a graph-theoretic clustering method is applied to seek the primary grouping among the images. The consistent subsets within the groups are identified by inferring category templates. Given the input, the output of the approach is a partition of the images into a set of learned categories. We demonstrate this approach on a field experiment for a powerline inspection robot. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kadlec:2008:ijcnn,
author = "Petr Kadlec and Bogdan Gabrys",
title = "Learnt Topology Gating Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0746.pdf},
url = {},
size = {},
abstract = {This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed Learnt Topology Gating Artificial Neural Networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are Artificial Neural Networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shahjahan2:2008:ijcnn,
author = "Md. Shahjahan and Kafi M. Nahin and Md. Shamim Ahsan and K. Murase",
title = "An Implementation of On-Line Traffic Information System via Short Message Service (SMS) for Bangladesh",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0747.pdf},
url = {},
size = {},
abstract = {In this paper, a vision-based on-line traffic information system is discussed. The system objectives are to detect levels of traffic congestion on certain roads in Dhaka City and to make this information available to the travelers. To achieve this task, multiple Web Cams will be installed on designated roads. The system will capture digital images of the passing by traffic, analyze these images, and reach a clear decision about number of car. Users will then be able to reach this data by using the short messaging service in their mobile phones. Basically, the system is divided into three independent, yet interacting modules: the image capturing module which will automate the capture of images, the digital image processing module which will process the images, and the short message service (known as SMS) server module which will receive SMSs from a user and reply back to him by an SMS. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xiaojing:2008:ijcnn,
author = "Guo Xiaojing and Wu Xiaopei and Zhang Dexiang",
title = "Motor Imagery EEG Detection by Empirical Mode Decomposition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0748.pdf},
url = {},
size = {},
abstract = {The paper investigates the possibility of using empirical mode decomposition (EMD) method to detect the mu rhythm of motor imagery EEG signal. Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to detect the mu rhythm during left and right hand movement imagination. By analyzing the instantaneous amplitude and instantaneous frequency of the intrinsic mode functions (IMFs), the mu rhythm can be detected. And by Hilbert marginal spectrum, the ERD/ERS phenomenon of mu rhythm can be found. The results in this paper demonstrate that the EMD method is a effective time-frequency analysis tool for non-stationary EEG signal. },
keywords = { Empirical mode decomposition (EMD), Intrinsic mode functions (IMFs), motor imagery EEG, Brain-Computer Interface (BCI), instantaneous frequency, instantaneous amplitude, Hilbert marginal spectrum.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Surówka:2008:ijcnn,
author = "Grzegorz Surówka ",
title = "Inductive Learning of Skin Lesion Images for Early Diagnosis of Melanoma",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0749.pdf},
url = {},
size = {},
abstract = {We take advantage of natural induction methods to build classifiers of the pigmented skin lesion images. This methodology can be treated as a non-invasive approach to early diagnosis of melanoma. We use the AQ21 application, which is based on the attributional calculus, to discover patterns in the skin images. Our classifier has good efficiency and may potentially be an important diagnostic aid. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hsu2:2008:ijcnn,
author = "Yu-Su Hsu and Tang-Jung Chiu and Hsin Chen",
title = "Real-Time Recognition of Continuous-Time Biomedical Signals Using the Diffusion Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0753.pdf},
url = {},
size = {},
abstract = {Real-time recognition of multichannel, continuoustime physiological signals has been crucial for the development of implantable biomedical devices. This work investigates the feasibility of using the Diffusion Network, a stochastic recurrent neural network, to recognise continuous-time biomedical signals. In addition, a hardware-friendly approach for achieving real-time recognition is proposed and tested with both artificial and real biomedical data. Based on this approach, the Diffusion Network is demonstrated to exhibit great tolerance against noise and drifts in continuous-time signals being classified. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo5:2008:ijcnn,
author = "Yimo Guo and Zhengguang Xu ",
title = "Local Binary Pattern with New Decomposition Method for Face Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0755.pdf},
url = {},
size = {},
abstract = {As face is a topological object, spatial contents contained in facial images (i.e. eyes, nose...) play an important role in feature extraction. To preserve spatial information, region decomposition is an essential step in face recognition for local feature based methods. In this paper, a new region decomposition method is proposed based on Cellular Neural Network (CNN). This method, called Face Penta-Chotomy (FPC), can be factorized into two parts. First, a stable facial region is extracted by a CNN template. Then other four regions are depicted according to the stable facial region and facial proportion. The local binary pattern (LBP) is adopted as the region descriptor. This method is evaluated by conducting experiments on the Yale face database B and ORL database. Besides, it compared with six state-of-the-art methods. From experimental results, it outperforms all the compared methods and the feature dimension can be significantly reduced compared with the conventional uniform region decomposition method. Moreover, the proposed method is demonstrated to be robust under single training condition. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wall:2008:ijcnn,
author = "Julie A. Wall and Liam J. McDaid and Liam P. Maguire and Thomas M. McGinnity ",
title = "Spiking Neuron Models of the Medial and Lateral Superior Olive for Sound Localisation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0756.pdf},
url = {},
size = {},
abstract = {Sound localisation is defined as the ability to identify the position of a sound source. The brain employs two cues to achieve this functionality for the horizontal plane, interaural time difference (ITD) by means of neurons in the medial superior olive (MSO) and interaural intensity difference (IID) by neurons of the lateral superior olive (LSO), both located in the superior olivary complex of the auditory pathway. This paper presents spiking neuron architectures of the MSO and LSO. An implementation of the Jeffress model using spiking neurons is presented as a representation of the MSO, while a spiking neuron architecture showing how neurons of the medial nucleus of the trapezoid body interact with LSO neurons to determine the azimuthal angle is discussed. Experimental results to support this work are presented. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wade:2008:ijcnn,
author = "John J. Wade and Liam J. McDaid and Jose A. Santos and Heather M. Sayers",
title = "SWAT: An Unsupervised SNN Training Algorithm for Classification Problems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0757.pdf},
url = {},
size = {},
abstract = {The work presented in this paper merges the Bienenstock-Cooper-Munro (BCM) learning rule with Spike Timing Dependent Plasticity (STDP) to develop a training algorithm for a Spiking Neural Network (SNN), stimulated using spike trains. The BCM rule is used to modulate the height of the plasticity window, associated with STDP. The SNN topology uses a single training neuron in the training phase where all classes are passed to this neuron, and the associated weights are subsequently mapped to the classifying output neurons: the weights are proportionally distributed across the output neurons to reflect similarities in the input data. The training algorithm also includes both exhibitory and inhibitory facilitating dynamic synapses that create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. The network is benchmarked against the non-linearly separable IRIS data set problem and results presented in the paper show that the proposed training algorithm exhibits a convergence accuracy comparable to other SNN training algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhao3:2008:ijcnn,
author = "Qibin Zhao and Liqing Zhang and Andrzej Cichocki and Jie Li",
title = "Incremental Common Spatial Pattern Algorithm for BCI",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0758.pdf},
url = {},
size = {},
abstract = {A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm. To overcome this, in this paper, we propose the Incremental Common Spatial Pattern (ICSP) algorithm which performs the adaptive feature extraction on-line. This method allows us to perform the online adjustment of spatial filter. This procedure helps the BCI system robust to possible non-stationarity of the EEG data. We test our method to data from BCI motor imagery experiments, and the results demonstrate the good performance of adaptation of the proposed algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Oentaryo2:2008:ijcnn,
author = "Richard J. Oentaryo and Michel Pasquier",
title = "A Reduced Rule-Based Localist Network for Data Comprehension",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0759.pdf},
url = {},
size = {},
abstract = {Localist networks and especially neuro-fuzzy systems constitute promising techniques for data comprehension, but generally exhibit poor system interpretability and generalization ability. This paper aims at addressing the issues through a novel localist Reduced Fuzzy Cerebellar Model Articulation Controller (RFCMAC), that models the two-stage development of cortical memories in the human brain to compress and refine the formulated (fuzzy) rule base respectively. The proposed mechanisms allow the RFCMAC associative memory to induce a concise, interpretable rule base, and at the same time to improve generalization, fostering in turn system scalability and robustness. Experimental results on several benchmark tasks have demonstrated the potential of the proposed system as an effective tool for understanding data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Almeida2:2008:ijcnn,
author = "Gustavo M. de Almeida and Marcelo Cardoso and Danilo C. Rena and Song W. Park",
title = "Graphical Representation of Cause-Effect Relationships among Chemical Process Variables using a Neural Network Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0761.pdf},
url = {},
size = {},
abstract = {The visualization of relevant information from numerical data is not a natural task for human beings, mainly in case of multivariate systems. In compensation, graphical representations make the understanding easier since it explores the human capacity of processing visual information. Based on that, this study constructs a cause-effect map relating effects of operating process variables over the steam generated by a boiler. This is done after the identification of a neural predictive model for this response. The use of such data-driven technique is due to its capacity of performing a non linear input-output mapping given a reliable data base. The case study is based on the operations of a chemical recovery boiler belonging to a Kraft pulp mill located in Brazil. The utility of the obtained map is clear, once the visualization of the contributions of each process variable over the output steam, from this graphical representation, is more intuitive. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sorjamaa:2008:ijcnn,
author = "Antti Sorjamaa and Yoan Miche and Robert Weiss and Amaury Lendasse",
title = "Long-Term Prediction of Time Series Using NNE-Based Projection and OP-ELM",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0762.pdf},
url = {},
size = {},
abstract = {This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then, after the network is first created using the original ELM, the selection of the most relevant nodes is performed by using a Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances, leading to an Optimally-Pruned ELM (OP-ELM). Finally, the prediction accuracy of the global methodology is demonstrated using the ESTSP 2008 Competition and Poland Electricity Load datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Joshi:2008:ijcnn,
author = "Sachin Joshi and Kishore Prahallad and B. Yegnanarayana",
title = "AANN-HMM Models for Speaker Verification and Speech Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0764.pdf},
url = {},
size = {},
abstract = {Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition.We show that AANN models produce comparable performance with that of GMM based speaker verification and speech recognition. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Valdes:2008:ijcnn,
author = "A. Valdes and K. Khorasani",
title = "Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for the Attitude Control System of a Satellite",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0765.pdf},
url = {},
size = {},
abstract = {The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) of a satellite. The goal is to determine the occurrence of a fault in any one of the multiple thrusters that are employed in the attitude control subsystem of a satellite, and further to localize which PPT is faulty. In order to accomplish these objectives, a multilayer perceptron network embedded with dynamic neurons is proposed. Based on a given set of input-output data collected from the electrical circuit of the PPTs, the dynamic network parameters are adjusted to minimize the output estimation error. A Confusion Matrix approach is used to measure the effectiveness of our proposed dynamic neural network-based fault detection and isolation (FDI) scheme under various fault scenarios. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Satheeshkumar:2008:ijcnn,
author = "J. Satheeshkumar and S. Arumugaperumal and R. Rajesh and C. Kesavadas",
title = "Does Brain React On Indian Music? - A Functional Magnetic Resonance Imaging Study",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0766.pdf},
url = {},
size = {},
abstract = {Listening to music, as per clinical neuro science, involves many cognitive components with distinct brain substrates and its study has advanced greatly in the last three decades. But the studies of Indian music and its influence in the brain have not yet been studied. This article presents sequence of image processing steps using statistical parametric mapping for the analysis of fMRI brain structures for studying the influence of two Indian ragas namely Sankarabnam and Madhyamavathi on a non-musician brain. The results shows that ragas have a very good influence on non-musician and also shows that raga named Madhyamavathi has influenced the subject more than Sankarabranam. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bhagat:2008:ijcnn,
author = "K. K. Kiran Bhagat and Stefan Wermter and Kevin Burn",
title = "Hybrid Learning Architecture for Unobtrusive Infrared Tracking Support",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0767.pdf},
url = {},
size = {},
abstract = {The system architecture presented in this paper is designed for helping an aged person to live longer independently in their own home by detecting unusual and potentially hazardous behaviours. The system consists of two major components. The first component is the tracking part which is responsible for monitoring the movements of the person within the home, while the second part is a learning agent which is responsible for learning the behavioural patterns of the person. For the tracking part of the system a simulation portraying a virtual room with passive infrared sensors has been designed, while for the learning agent a hybrid architecture has been implemented. The hybrid architecture consists of a Markov Chain Model, Template Matching, Fuzzy Logic and Memory-Based reasoning techniques. The hybrid structure was selected because it combined the strengths of the constituent algorithms and because it supports the learning with limited training data. The resultant system was able to not only classify between the normal and the abnormal paths but was also able to distinguish between different normal routes. We claim that passive infrared tracking combined with a hybrid learning architecture has potential for adaptive unobtrusive tracking support. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gill:2008:ijcnn,
author = "Arjun Singh Gill ",
title = "A Novel Low Complexity Speech Recognition Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0768.pdf},
url = {},
size = {},
abstract = {In the field of Digital Speech Recognition powerful ASR (Automatic Speech Recognizer) systems have been developed which employ highly intricate algorithms like the HMM, DTW and Neural Network based algorithms capable of recognizing up to 1000 different words. Their high complexity and computation requirements prove to be superfluous for less demanding tasks. In this paper is proposed a simple, less aggressive and computationally efficient algorithm that can parallel any of the above algorithms' ability to distinguish a few words (typically up to 5 different words) but demands considerably less computational power making it suitable for embedded systems. Also discussed is the technique of ``Menu Driven'' control where the number of words that can be recognized, pose no frontier to the number of tasks that can be performed by using very few (5 or less) words. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yau:2008:ijcnn,
author = "Chi-Yung Yau and Kevin Burn and Stefan Wermter ",
title = "A Neural Wake-Sleep Learning Architecture for Associating Robotic Facial Emotions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0769.pdf},
url = {},
size = {},
abstract = {A novel wake-sleep learning architecture for processing a robot's facial expressions is introduced. According to neuroscience evidence, associative learning of emotional responses and facial expressions occurs in the brain in the amygdala. Here we propose an architecture inspired by how the amygdala receives information from other areas of the brain to discriminate it and generate innate responses. The architecture is composed of many individual Helmholtz machines using the wake-sleep learning algorithm for performing information transformation and recognition. The Helmholtz machine is used since its re-entrant connections support both supervised and unsupervised learning. Potentially it can explain some aspects of human learning of emotional concepts and experience. In this research, a robotic head's facial expression dataset is used. The objective of this learning architecture is to demonstrate the neural basis for the association of recognized facial expressions and linguistic emotion labels. It implies the understanding of emotions from observation and is further used to generate facial expressions. In contrast with other facial expression recognition research, this work concentrates more on emotional information processing and neural concept development, rather than a technical recognition task. This approach has a lot of potential to contribute towards neurally inspired emotional experience in robotic systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Carvalho:2008:ijcnn,
author = "Cesar A. M. Carvalho and George D. C. Cavalcanti",
title = "An Artificial Neural Network Approach for User Class-Dependent Off-Line Sentence Segmentation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0772.pdf},
url = {},
size = {},
abstract = {In this paper, we present an Artificial Neural Network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through experimentation, which was performed using the IAM database, it was determined that the proposed approach achieved better results than the traditional Gap Metric approach for handwriting sentence segmentation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Rast:2008:ijcnn,
author = "Alexander D. Rast and Shufan Yang and Mukaram Khan and Steve B. Furber ",
title = "Virtual Synaptic Interconnect Using an Asynchronous Network-on-Chip",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0773.pdf},
url = {},
size = {},
abstract = {Given the limited current understanding of the neural model of computation, hardware neural network architectures that impose a specific relationship between physical connectivity and model topology are likely to be overly restrictive. Here we introduce, in the SpiNNaker chip, an alternative approach: a mappable virtual topology using an asynchronous network-on-chip (NoC) that decouples the ``logical'' connectivity map from the physical wiring. Borrowing the established digital RAM model for synapses, we develop a concurrent memory access channel optimised for neural processing that allows each processing node to perform its own synaptic updates as if the synapses were local to the node. The highly concurrent nature of interconnect access, however, requires careful design of intermediate buffering and arbitration. We show here how a locally buffered, one-transaction-per-node model with multiple synapse updates per transaction enables the local node to offload continuous burst traffic from the NoC, allowing for a hardware-efficient design that supports biologically realistic speeds. The design not only presents a flexible model for neural connectivity but also suggests an ideal form for general-purpose high-performance on-chip interconnect. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lau:2008:ijcnn,
author = "Javy H. Y. Lau and Bertram E. Shi",
title = "Improved Illumination Invariance Using a Colour Edge Representation Based on Double Opponent Neurons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0774.pdf},
url = {},
size = {},
abstract = {We describe an evaluation framework that provides a quantitative measure on the performance of a neural network colour constancy model. In this framework, the responses of three models of colour constancy to a set of colour edges under varying illuminating conditions are computed. We study a model based on Double Opponent cells, as well as two variants of the Retinex model. Evaluation metrics on the models' capabilities to discriminate among different colour edges and resist illuminant induced changes are measured. Using this framework, we confirm the advantage of incorporating spectral opponency into the colour constancy model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen12:2008:ijcnn,
author = "Weiliang Chen and Rod Adams and Lee Calcraft",
title = "Connectivity Graphs and the Performance of Sparse Associative Memory Models",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0775.pdf},
url = {},
size = {},
abstract = {This paper investigates the relationship between network connectivity and associative memory performance using high capacity associative memory models with different types of sparse networks. We found that the clustering of the network, measured by Clustering Coefficient and Local Efficiency, have a strong linear correlation to its performance as an associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Casey:2008:ijcnn,
author = "Matthew C. Casey and Athanasios Pavlou",
title = "A Behavioral Model of Sensory Alignment in the Superficial and Deep Layers of the Superior Colliculus",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0776.pdf},
url = {},
size = {},
abstract = {The ability to combine sensory information is an important attribute of the brain. Multisensory integration in natural systems suggests that a similar approach in artificial systems may be important. Multisensory integration is exemplified in mammals by the superior colliculus (SC), which combines visual, auditory and somatosensory stimuli to shift gaze. However, although we have a good understanding of the overall architecture of the SC, as yet we do not fully understand the process of integration. While a number of computational models of the SC have been developed, there has not been a larger scale implementation that can help determine how the senses are aligned and integrated across the superficial and deep layers of the SC. In this paper we describe a prototype implementation of the mammalian SC consisting of self-organizing maps linked by Hebbian connections, modeling visual and auditory processing in the superficial and deep layers. The model is trained on artificial auditory and visual stimuli, with testing demonstrating the formation of appropriate spatial representations, which compare well with biological data. Subsequently, we train the model on multisensory stimuli, testing to see if the unisensory maps can be combined. The results show the successful alignment of sensory maps to form a multisensory representation. We conclude that, while simple, the model lends itself to further exploration of integration, which may give insight into whether such modeling is of benefit computationally. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Almeida3:2008:ijcnn,
author = "Carlos W. D. de Almeida and Renata M. C. R. de Souza and Nicomedes L. Cavalcanti Júnior",
title = "Image Retrieval Using the Curvature Scale Space (CSS) Descriptor and the Self-Organizing Map (SOM) Model Under Scale Invariance",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0777.pdf},
url = {},
size = {},
abstract = {In a previous work [4], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under different scales. The shape features of images are represented by CSS images extracted from, for example, a large database and represented by median vectors that constitutes the training data set for a SOM neural network which, in turn, will be used for performing efficient image retrieval. Experimental results using a benchmark database are presented to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy and retrieval time assessed in the framework of a Monte Carlo experience. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wahid:2008:ijcnn,
author = "Khan Wahid and Seok-Bum Ko and Daniel Teng ",
title = "Efficient Hardware Implementation of an Image Compressor for Wireless Capsule Endoscopy Applications",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0778.pdf},
url = {},
size = {},
abstract = {The paper presents an area- and power-efficient implementation of an image compressor for wireless capsule endoscopy application. The architecture uses a direct mapping to compute the two-dimensional Discrete Cosine Transform which eliminates the need of transpose operation and results in reduced area and low processing time. The algorithm has been modified to comply with the JPEG standard and the corresponding quantization tables have been developed and the architecture is implemented using the CMOS 0.18um technology. The processor costs less than 3.5k cells, runs at a maximum frequency of 150 MHz, and consumes 10 mW of power. The test results of several endoscopic colour images show that higher compression ratio (over 85percent) can be achieved with high quality image reconstruction (over 30 dB). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hu4:2008:ijcnn,
author = "Xiao Hu and Raj Subbu and Piero Bonissone and Hai Qiu and Naresh Iyer ",
title = "Multivariate Anomaly Detection in Real-World Industrial Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0779.pdf},
url = {},
size = {},
abstract = {Anomaly detection is a critical capability enabling condition-based maintenance (CBM) in complex real-world industrial systems. It involves monitoring changes to system state to detect änomalous" behavior. Timely and reliable detection of anomalies that indicate faulty conditions can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses and causes secondary damage to the system leading to downtime. When an anomaly is identified, it is important to isolate the source of the fault so that appropriate maintenance actions can be taken. In this paper, we introduce effective multivariate anomaly detection techniques and methods that allow fault isolation. We present experimental results from the application of these techniques to a high-bypass commercial aircraft engine. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gudmundsson:2008:ijcnn,
author = "Steinn Gudmundsson and Thomas Philip Runarsson and Sven Sigurdsson ",
title = "Support Vector Machines and Dynamic Time Warping for Time Series",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0780.pdf},
url = {},
size = {},
abstract = {Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example be represented by its similarity to all the examples in this set and then apply a conventional SVM to this transformed data. Dynamic time warping (DTW) is a well established distance measure for time series but has been of limited use in SVMs since it is not obvious how it can be used to derive a PSD kernel. The feasibility of the similarity based approach for DTW is investigated by applying the method to a large set of time-series classification problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bastos-Filho:2008:ijcnn,
author = "Carmelo J. A. Bastos-Filho and Wesnaida H. Schuler and Adriano L. I. Oliveira",
title = "A Fast and Reliable Routing Algorithm Based on Hopfield Neural Networks Optimized by Particle Swarm Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0783.pdf},
url = {},
size = {},
abstract = {Routing is very important for computer networks because it is one of the main factors that influences network performance. In this paper, we propose an improved intelligent method for routing based on Hopfield Neural Networks (HNN), which uses a discrete equation and the Particle Swarm Optimization (PSO) technique to optimize the HNN parameters. The fitness function for the PSO algorithm used here is a combination of the number of iterations for convergence and the percentage error when the HNN method tries to find the best path in a communication network. The simulation results show that PSO is a reliable approach to optimize the Hopfield network for routing in computer networks, since this method results in fast convergence and produces accurate results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva4:2008:ijcnn,
author = "Leandro Augusto da Silva and Humberto Sandmann and Emilio Del-Moral-Hernandez",
title = "A Self-Organizing Architecture of Recursive Elements for Continuous Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0786.pdf},
url = {},
size = {},
abstract = {This paper describes how recursive nodes with rich dynamics can be explored in a self-organizing artificial network for continuous learning tasks. The purpose of inserting the recursive elements is introducing chaos behavior in a modified Self-Organizing Map (SOM). This new structure is called CSOM. It incorporates some of the main features of SOM, but it also improves the capability of cluster input patterns through increasing the winning opportunities of the units. The proposal is to use the Lyapunov Exponent value to define the winner unit. In addition, the CSOM is introduced in continuous learning task, which is the capacity of learning a new pattern, without losing the patterns learned. The proposal addressed here is described, analyzed quantitatively and its performance is compared with that of conventional SOM. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Souto2:2008:ijcnn,
author = "Marcilio C. P. de Souto and Daniel S. A. de Araujo and Ivan G. Costa and Teresa B. Ludermir and Alexander Schliep",
title = "Comparative Study on Normalization Procedures for Cluster Analysis of Gene Expression Datasets",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0787.pdf},
url = {},
size = {},
abstract = {Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data. The results are presented in terms of the recovering of the true cluster structure as found by five different clustering algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Polettini:2008:ijcnn,
author = "Nicola Polettini and Diego Sona and Paolo Avesani",
title = "A Relational Cascade Correlation for Structured Outputs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0788.pdf},
url = {},
size = {},
abstract = {We propose a Relational Neural Network defined as a special instance of the Recurrent Cascade Correlation. The proposed model is designed to deal with classification tasks where classes are organized into generic graphs (e.g., taxonomies, ontologies, etc.). The open challenge is to exploit the knowledge encoded in the relationships among the classes. This is particularly useful when there are many classes poorly represented by labeled examples. Exploiting the relationships we increase the bias, making the generalization more robust. The novelty of the proposed model can be seen from two different perspectives. On one hand, the temporal encoding of the standard recurrent networks is revised with a notion of non-stationary structural unfolding. On the other hand, it can be seen as a novel constructive algorithm that generates the neural network architecture exploiting the class structure. We present the results of an empirical evaluation on a hierarchical document classification task. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Petlenkov:2008:ijcnn,
author = "Eduard Petlenkov and Sven Nomm and Jüri Vain and Fujio Miyawaki",
title = "Application of Self Organizing Kohonen Map to Detection of Surgeon Motions During Endoscopic Surgery",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0789.pdf},
url = {},
size = {},
abstract = {Segmentation of the surgeon's hand movements during the surgery into more primitive parts and recognition of those parts using Kohonen map is discussed in present paper. Main advantages of the proposed approach are that it allows to take into account dynamical characteristics of the hand movements and exclude probability of human error in building etalon segmentation. Ability to recognize current action of the surgeon has a crucial importance in developing a robot able to assist surgeon during the endoscopic surgical operation. One of the possible ways is to predefine a set of possible surgeon's actions and provide a recognition algorithm explored in the framework of present contribution. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jin3:2008:ijcnn,
author = "Xin Jin and Steve B. Furber and John V. Woods",
title = "Efficient Modelling of Spiking Neural Networks on a Scalable Chip Multiprocessor",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0790.pdf},
url = {},
size = {},
abstract = {we propose a system based on the Izhikevich model running on a scalable chip multiprocessor - SpiNNaker - for large-scale spiking neural network simulation. The design takes into account the requirements for processing, storage, and communication which are essential to the efficient modelling of spiking neural networks. To gain a speedup of the processing as well as saving storage space, the Izhikevich model is implemented in 16-bit fixed-point arithmetic. An approach based on using two scaling factors is developed, making the precision comparable to the original. With the two scaling factors scheme, all of the firing patterns by the original model can be reproduced with a much faster execution speed. To reduce the communication overhead, rather than sending synaptic weights on communicating, we only send out event packets to indicate the neuron firings while holding the synaptic weights in the memory of the post-synaptic neurons, which is so-called event-driven algorithm. The communication based on event packets can be handled efficiently by the multicast system supported by the SpiNNaker machine. We also describe a system level model for spiking neural network simulation based on the schemes above. The model has been functionally verified and experimental results are included. An analysis of the performance of the whole system is presented at the end of the paper. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Levine:2008:ijcnn,
author = "Daniel S. Levine and Leonid I. Perlovsky",
title = "A Network Model of Rational Versus Irrational Choices on a Probability Maximization Task",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0791.pdf},
url = {},
size = {},
abstract = {Humans have a drive to maximize knowledge of the world, yet decision making data also suggest a contrary drive to minimize cognitive effort using simplifying heuristics. The trade-off between maximizing knowledge and minimizing effort is modeled by simulation of a challenging decision task. The task is to choose which of two gambles has the highest probability of success when the alternative with higher success probability also has lower success frequency. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Harb:2008:ijcnn,
author = "Moufid Harb and Rami Abielmona and Emil Petriu and Kamal Naji",
title = "Neural Control System of a Mobile Robot",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0792.pdf},
url = {},
size = {},
abstract = {Mobile robots could play a significant role in places where it is impossible for the human to work. In such environments, neural networks, instead of traditional methods, are suitable solutions to locally navigate and recognize the environment's subspaces. In order to learn and perform two important functions ``environmental recognition'' and ``local navigation'', multi-layered neural networks are trained to process distance measurements received from a laser rangefinder. This paper will focus on a computer based design and test of this neural system, that includes three neural controllers for local navigation, and two neural networks for environmental recognition, fed off-line by a simulated model of a laser range-finder. These neural networks are the major components of a control system that performs a global neural navigation of a mobile robot, which could be used to perform industrial missions within industrial environments. This control system can guide a mobile robot to track its predefined path to arrive to its final goal through a set of sub-goals, or autonomously plan its path to arrive to the desired final goal, and to avoid obstacles that are found along the way. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wong2:2008:ijcnn,
author = "Aaron S. W. Wong and Stephan K. Chalup ",
title = "Towards Visualisation of Sound-Scapes Through Dimensionality Reduction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0793.pdf},
url = {},
size = {},
abstract = {Sound-scapes are useful for understanding our surrounding environments in applications such as security, source tracking or understanding human computer interaction. Accurate position or localisation information from sound-scape samples consists of many channels of high dimensional acoustic data. In this paper we demonstrate how to obtain a visual representation of sound-scapes by applying dimensionality reduction techniques to a range of artificially generated sound-scape datasets. Linear and non-linear dimensionality techniques were compared including principle component analysis (PCA), multidimensional scaling (MDS), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Results obtained by applying the dimensionality reduction techniques led to visual representations of affine positions of the sound source on its sound-scape manifold. These displayed clearly the order relationships of angles and intensities of the generated sound-scape samples. In a simple classification task with the artificial sound data, the successful combination of dimensionality reduction and classifier methods are demonstrated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Stahlbock:2008:ijcnn,
author = "Robert Stahlbock ",
title = "Neural Classification Approach for Short Term Forecast of Exchange Rate Movement with Point and Figure Charts",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0795.pdf},
url = {},
size = {},
abstract = {In the domain of classification and forecasting tasks, artificial neural networks (ANNs) are prominent data mining methods. Neural network paradigms like learning vector quantization (LVQ) are suitable for solving classification problems. In this paper, we combine LVQ with the popular Point & Figure (P&F) chart analysis applied to a one day forecast of the exchange rate between Euro (EUR) and US Dollar (USD). We present two different P&F encoding schemes and analyze the classification accuracy and results of a trading system fed with our results from the LVQ. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Khan:2008:ijcnn,
author = "M. M. Khan and D. R. Lester and L. A. Plana and A. Rast and X. Jin and E. Painkras and S. B. Furber ",
title = "SpiNNaker: Mapping Neural Networks onto a Massively-Parallel Chip Multiprocessor",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0796.pdf},
url = {},
size = {},
abstract = {SpiNNaker is a novel chip - based on the ARM processor - which is designed to support large scale spiking neural networks simulations. In this paper we describe some of the features that permit SpiNNaker chips to be connected together to form scalable massively-parallel systems. Our eventual goal is to be able to simulate neural networks consisting of 109 neurons running in `real time', by which we mean that a similarly sized collection of biological neurons would run at the same speed.In this paper we describe the methods by which neural networks are mapped onto the system, and how features designed into the chip are to be exploited in practice. We will also describe the modelling and verification activities by which we hope to ensure that, when the chip is delivered, it will work as anticipated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kasturi:2008:ijcnn,
author = "Jyotsna Kasturi and Raj Acharya",
title = "A New Information-Theoretic Dissimilarity for Clustering Time-Dependent Gene Expression Profiles Modeled with Radial Basis Functions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0797.pdf},
url = {},
size = {},
abstract = {The study and inference of biological pathways and gene regulation mechanisms has become a vital component of modern medicine and drug discovery. Gene expression studies make it possible to understand these mechanisms by simultaneously measuring the expression level of thousands of genes. These data though rich in information are also prone to many quality control issues that ultimately result in noisy data. A new method to smooth the data and measure expression dissimilarity between genes is proposed in this paper. A new dissimilarity measure is defined as an approximation of the Kullback-Leibler divergence between mixture models. Further, a noise reduction method is also proposed for use with data from time-course experiments. Results from real data and simulated data demonstrate that the method is well suited for clustering gene expression profiles. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva5:2008:ijcnn,
author = "Kelly P. Silva and Francisco A. T. de Carvalho and M. Csernel",
title = "Clustering of Symbolic Data Through a Dissimilarity Volume Based Measure",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0799.pdf},
url = {},
size = {},
abstract = {The recording of symbolic data has become a common practice with the recent advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cao2:2008:ijcnn,
author = "Yuan Cao and Haibo He",
title = "Learning from Testing Data: A New View of Incremental Semi-Supervised Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0800.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a novel method for incremental semi-supervised learning. Unlike the traditional way of incremental learning or semi-supervised learning, we try to answer a more challenging question: given inadequate labeled training data, can one use the unlabeled testing data to improve the learning and prediction accuracy? The objective here is to reinforce the learning system trained offline through online incremental semi-supervised learning based on the testing data distribution. To do this, we propose an iterative algorithm that can adaptively recover the labels for testing data based on their confidence levels, and then extend the training population by such recovered data to facilitate learning and prediction. Multiple hypotheses are developed based on different learning capabilities of different recovered data sets, and a voting method is used to integrate the decisions from different hypotheses for the final predicted labels. We compare the proposed algorithm with bootstrap aggregating (bagging) method for performance evaluation. Simulation results on various real-world data sets illustrate the effectiveness of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Park2:2008:ijcnn,
author = "Sunho Park and Seungjin Choi",
title = "Gaussian Process Regression for Voice Activity Detection and Speech Enhancement",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0802.pdf},
url = {},
size = {},
abstract = {Gaussian process (GP) model is a flexible nonparametric Bayesian method that is widely used in regression and classification. In this paper we present a probabilistic method where we solve voice activity detection (VAD) and speech enhancement in a single framework of GP regression, modeling clean speech by a GP smoother. Optimized hyperparameters in GP models lead us to a novel VAD method since learned lengthscale parameters in covariance functions are much different between voiced and unvoiced frames. Clean speech is estimated by posterior means in GP models. Numerical experiments confirm the validity of our method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Brodu:2008:ijcnn,
author = "Nicolas Brodu ",
title = "Multifractal Feature Vectors for Brain-Computer Interfaces",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0803.pdf},
url = {},
size = {},
abstract = {This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI competitions II and III. The feature extraction can be performed in real time with low-cost discrete wavelet transforms. Classification results obtained with the new feature vectors are close to the state of art techniques, while using a different information. Combining the new multifractal feature vector with existing ones may result in better performances, up to 5percent in the present case. This work thus offers an alternative to the usual feature-extraction techniques, and opens new possibilities in the field of Brain-Computer interfaces. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang10:2008:ijcnn,
author = "Qingguo Wang and Yi Shang and Dong Xu",
title = "A New Clustering-Based Method for Protein Structure Selection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0804.pdf},
url = {},
size = {},
abstract = {In protein tertiary structure prediction, it is a crucial step to select near-native structures from a large number of candidate structural models. Despite much effort to tackle the problem of protein structure selection, the discerning power of current scoring functions is still unsatisfactory.In this paper, we developed a new clustering-based method for selecting near-native protein structures. Our method consists of three phases: filtering, clustering and cluster reduction, and centroid construction. Given a set of Cα protein structures, we apply one or multiple existing scoring functions to filter out bad structures. Then, we group the remaining structures into clusters based on pair-wise similarity measured by RMSD. Each cluster is reduced iteratively to remove outliers and bad structures. Finally, we construct a centroid for each cluster by applying multi-dimensional scale techniques. The centroids are the final models. In experiments, we applied our method to a test set of representative proteins and obtained significant improvement over existing methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mencía:2008:ijcnn,
author = "Eneldo Loza Mencía and Johannes Fürnkranz",
title = "Pairwise Learning of Multilabel Classifications with Perceptrons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0806.pdf},
url = {},
size = {},
abstract = {Multiclass multilabel perceptrons (MMP) have been proposed as an efficient incremental training algorithm for addressing a multilabel prediction task with a team of perceptrons. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative technique that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. Our evaluation on four multilabel datasets shows that the multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Santana2:2008:ijcnn,
author = "Laura E. A. Santana and Anne M. P. Canuto",
title = "An Analysis of Data Distribution Methods in Classifier Combination Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0807.pdf},
url = {},
size = {},
abstract = {In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this paper, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, five different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shokri:2008:ijcnn,
author = "Maryam Shokri and Hamid R. Tizhoosh and Mohamed S. Kamel",
title = "Tradeoff Between Exploration and Exploitation of OQ(λ) with Non-Markovian Update in Dynamic Environments",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0809.pdf},
url = {},
size = {},
abstract = {This paper presents some investigations on tradeoff between exploration and exploitation of opposition-based Q(λ) with non-Markovian update (NOQ(λ) in a dynamic environment. In the previous work the authors applied NOQ(λ) to the deterministic GridWorld problem. In this paper, we have implemented the NOQ(λ) algorithm for a simple elevator control problem to test the behavior of the algorithm for nondeterministic and dynamic environment. We also extend the NOQ(λ) algorithm by introducing the opposition weight to find a better tradeoff between exploration and exploitation for the NOQ(λ) technique. The value of the opposition weight increases as the number of steps increases. Hence, it has more positive effects on the Q-value updates for opposite actions as the learning progresses. The performance of NOQ(λ) method is compared with Q(λ) technique. The experiments indicate that NOQ(λ) performs better than Q(λ). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lee3:2008:ijcnn,
author = "Jong Chan Lee and Wu Jun and Won Don Lee",
title = "Deterministic AdaBoost Algorithm Based on FLDF",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0811.pdf},
url = {},
size = {},
abstract = {AdaBoost is an algorithm with a procedure of selecting the data events from a dataset at each iteration sequence. The data events are selected stochastically using a random number generator. In this paper, a deterministic AdaBoost algorithm is proposed in contrast to the usual stochastic one. For doing this we derive the modified Fisher's formulas moderated to the deterministic method. These formulas contain a scheme to treat data set with weight vector.To verify the performance of proposed algorithm, we compare with the results of different measurements with the deterministic and the stochastic method, by gradually increasing the prune rate and the number of weak learner in the network structure. Through the result of these experiments, we show that our proposed method has higher performance than typical stochastic one. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kim3:2008:ijcnn,
author = "Jungmin Kim and Yountae Kim and Sungshin Kim",
title = "An Accurate Localization for Mobile Robot Using Extended Kalman Filter and Sensor Fusion",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0812.pdf},
url = {},
size = {},
abstract = {This paper presents an accurate localization scheme for mobile robots based on the fusion of an ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve an accuracy of less than 100 mm. The INS consists of a yaw gyro and two wheel-encoders, and the U-SAT consists of four transmitters and a receiver. Besides the proposed localization method, we will fuse these in an extended Kalman filter. The performance of the localization was verified by simulation and two actual data sets (straight and curve) gathered from about 0.5 m/s of actual driving data. The localization methods used were general sensor fusion and sensor fusion through a Kalman filter using data from the INS. Through simulation and actual data analysis, the experiment shows the effectiveness of the proposed method for autonomous mobile robots. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Neris:2008:ijcnn,
author = "Marrony N. Neris and Alexandre J. Silva and Sarajane M. Peres and Franklin C. Flores",
title = "Self Organizing Maps and Bit Signature: A Study Applied on Signal Language Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0814.pdf},
url = {},
size = {},
abstract = {Self Organizing Map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition results are promising and are presented in this paper. More, some analysis about the combination ``SOM + bit signature'' improved our understanding about the characteristics of the LIBRAS signals and the conclusions are also listed in this paper. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Peng:2008:ijcnn,
author = "Ya-Fu Peng and Chih-Hui Chiu",
title = "The Implementation of Wheeled Robot Using Adaptive Output Recurrent CMAC",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0815.pdf},
url = {},
size = {},
abstract = {In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated to control the two-wheeled robot. The main purpose is to develop a self-dynamic balancing and motion control strategy. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters. Therefore, AORCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. Finally, the effectiveness of the proposed control system is verified by the experiments of the two-wheeled robot standing control. Experimental results show that the the two-wheeled robot can stand upright stably with uncertainty disturbance by using the proposed AORCMAC. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mohammed:2008:ijcnn,
author = "Dhafar S. Mohammed and Saeid Habibi and Danil Prokhorov",
title = "Adaptive Parameter Robust Estimation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0816.pdf},
url = {},
size = {},
abstract = {In this paper, we describe an adaptive technique for states and parameter estimation involving a combination of two methods, namely the Variable Structure Filter (VSF) and the Extend Kalman Filters (EKF).The VSF concept is a model-based robust state/parameter estimation. It has a secondary set of uncertainties that provide a measure of uncertainties in the filter model. It is not however an optimal method. When combined with the Kalman Filter, it provides near optimal solution (further to the assumption pertaining to the Kalman Filter). The combined strategy would then also benefit from the robustness and the additional indicators of performance of the VSF.These features of the combined strategy used for removing uncertainties in the estimation process by dynamic adaptation of the filter model.The modeling uncertainties in the combined VSF/EKF method are removed by using two forms of Neural Networks adaptation. These adaptation methods are based on the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Algorithm Of Pattern Extraction (ALOPEX). The use of dynamic adaption can significantly improve the performance of the estimation process.Other attractive features include computational simplicity, fast rate of convergence, robustness and stability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Goertzel:2008:ijcnn,
author = "Ben Goertzel ",
title = "A Pragmatic Path Toward Endowing Virtually-Embodied AIs with Human-Level Linguistic Capability",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0817.pdf},
url = {},
size = {},
abstract = {Current work is described wherein simplified versions of the Novamente Cognition Engine (NCE) are being used to control virtual agents in virtual worlds such as game engines and Second Life. In this context, an IRC (imitationreinforcement- correction) methodology is being used to teach the agents various behaviors, including simple tricks and communicative acts. Here we describe how this work may potentially be exploited and extended to yield a pathway toward giving the NCE robust, ultimately human-level natural language conversation capability. The pathway starts via using the current system to instruct NCE-controlled agents in semiosis and gestural communication; and then continues via integration of a particular sort of hybrid rule-based/statistical NLP system (which is currently partially complete) into the NCE-based virtual agent system, in such a way as to allow experiential adaptation of the rules underlying the NLP system, in a manner that builds on the agent's knowledge of semiosis and gesture. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Rao:2008:ijcnn,
author = "A. Ravishankar Rao and Guillermo A. Cecchi and Charles C. Peck and James R. Kozloski ",
title = "Efficient Segmentation in Multi-Layer Oscillatory Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0818.pdf},
url = {},
size = {},
abstract = {In earlier work, we derived the dynamical behavior of a network of oscillatory units described by the amplitude and phase of oscillations. The dynamics were derived from an objective function that rewards both the faithfulness and the sparseness of representation. After unsupervised learning, the network is capable of separating mixtures of inputs, and also segmenting the inputs into components that most contribute to the classification of a given input object.In the current paper, we extend our analysis to multi-layer networks, and demonstrate that the dynamical equations derived earlier can be successfully applied to multi-layer networks. The topological connectivity between the different layers are derived from biological observations in primate visual cortex, and consist of receptive fields that are topographically mapped between layers. We explore the role of feedback connections, and show that increasing the diffusivity of feedback connections significantly improves segmentation performance, but does not affect separation performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nitta:2008:ijcnn,
author = "Tohru Nitta and Sven Buchholz",
title = "On the Decision Boundaries of Hyperbolic Neurons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0820.pdf},
url = {},
size = {},
abstract = {In this paper, the basic properties, especially decision boundary, of the hyperbolic neurons used in the hyperbolic neural networks are investigated. And also, a nonsplit hyperbolic sigmoid activation function is proposed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nguyen:2008:ijcnn,
author = "G. H. Nguyen and A. Bouzerdoum and S. L. Phung",
title = "Efficient Supervised Learning with Reduced Training Exemplars",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0823.pdf},
url = {},
size = {},
abstract = {In this article, we propose a new supervised learning approach for pattern classification applications involving large or imbalanced data sets. In this approach, a clustering technique is employed to reduce the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed learning approach, two training algorithms are derived for feed-forward neural networks. These algorithms are implemented and tested on two pattern classification applications - skin detection and image classification. Experimental results show that with the proposed learning approach, it is possible to design networks in a fraction of time taken by the standard learning approach, without compromising the generalization ability and overall classification performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sharma:2008:ijcnn,
author = "Anand Sharma and Anthony Kuh",
title = "Class Document Frequency as a Learned Feature for Text Categorization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0824.pdf},
url = {},
size = {},
abstract = {Document classification uses different types of word weightings as features for representation of documents. In our findings we find the class document frequency, dfc, of a word is the most important feature in document classification. Machine learning algorithms trained with dfc of words show similar performance in terms of correct classification of test documents when compared to more complicated features. The importance of dfc is further verified when simple algorithms developed solely on the basis of dfc shows performance that compares closely with that of more complex machine learning algorithms. We also found improved performance when the dfc of links of documents in a class is used along with the dfc of the words of the document. We compared the algorithms for showing the importance of dfc on the Reuters-21578 text categorization test classification and the Cora data set. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lee4:2008:ijcnn,
author = "Hong Lee and Brijesh Verma",
title = "A Novel Multiple Experts and Fusion Based Segmentation Algorithm for Cursive Handwriting Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0825.pdf},
url = {},
size = {},
abstract = {This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation point to a threshold value. The threshold is set and calculated before the segmentation by scanning the stroke components in the word. Second expert checks for closed areas such as holes. Third expert validates segmentation points using a neural voting approach which is trained on segmented characters before validation process starts. Final expert is based on oversized segment analysis to detect possible missed segmentation points. The proposed algorithm has been implemented and the experiments on cursive handwritten text have been conducted. The results of the experiments are very promising and the overall performance of the algorithm is more effective than the other existing segmentation algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hsu3:2008:ijcnn,
author = "Chi-I Hsu and Meng-Long Shih and Biing-Wen Huang and Bing-Yi Lin and Chun-Nan Lin",
title = "Combining LISREL and Bayesian Network to Predict Tourism Loyalty",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0826.pdf},
url = {},
size = {},
abstract = {This study proposes an analytic approach that combines LISREL and Bayesian networks (BN) to examine factors influencing tourism loyalty and predict a tourist's loyalty level. LISREL is used to verify the hypothesized relationships proposed in the research model. Subsequently, the supported relationships are used as the BN network structure for prediction. 452 valid samples were collected from tourists with the tour experience of the Toyugi hot spring resort, Taiwan. Compared with other prediction methods, our approach yielded better results than those of back-propagation neural networks (BPN) or classification and regression trees (CART) for 10-fold cross-validation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang9:2008:ijcnn,
author = "Jin Zhang and Guang Li and Meng Hu and Jiaojie Li and Zhiyuan Luo",
title = "Recognition of Hypoxia EEG with a Preset Confidence Level Based on EEG Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0830.pdf},
url = {},
size = {},
abstract = {Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining KIII model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental results of classifying normal and hypoxia EEGs show that the method can set confidence level in advance for every prediction to control the risk of error effectively. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ninomiya:2008:ijcnn,
author = "Hiroshi Ninomiya and Qi-Jun Zhang",
title = "Particle with Ability of Local Search Swarm Optimization: PALSO for Training of Feedforward Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0831.pdf},
url = {},
size = {},
abstract = {This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robust neural network training purpose. Conventional neural network training algorithms based on the gradient descent often encounter local minima problems. Recently, some evolutionary algorithms are getting a lot more attention about global search ability but are less-accurate for complicated training task of neural networks. The proposed technique hybridizes local training algorithm based on quasi-Newton method with a recent global optimization algorithm called Particle Swarm Optimization (PSO). The proposed technique provides higher global convergence property than the conventional global optimization technique. Neural network training for some benchmark problems is presented to demonstrate the proposed algorithm. The proposed algorithm achieves more accurate and robust training results than the quasi-Newton method and the conventional PSOs. },
keywords = { Feedforward neural networks, Particle swarm optimization, quasi-Newton method, Hybrid algorithm },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Brajard:2008:ijcnn,
author = "Julien Brajard and Fouad Badran and Michel Crepon and Sylvie Thiria",
title = "Validation of Model Simulations with Respect to in Situ Observations by the use of Probabilistic Estimations",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0836.pdf},
url = {},
size = {},
abstract = {The present work addresses the problem of validation of a synthetic dataset with respect to observations. It gives an index that determines locally how much a region of the synthetic dataset fits the observations. The method uses an estimation of the probability density function computed with the probabilistic self-organizing maps. Then, an index F was defined to quantify the areas of the synthetic datasets that correspond to the observations.The method was first applied to a ''toy'' example in 2 dimensions to see its potentiality and then applied to two real datasets of optics measurements of the surface ocean. The method allowed to characterize some simulations that have not been encountered during ship campaigns. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Achler:2008:ijcnn,
author = "Tsvi Achler and Cyrus Omar and Eyal Amir",
title = "Shedding Weights: More with Less",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0837.pdf},
url = {},
size = {},
abstract = {Traditional connectionist models place an emphasis on learned weights. Based on neurobiological evidence, a new approach is developed and experimentally shown to be more robust for disambiguating novel combinations of stimuli. It does not require variable weights and avoids many training related issues. This approach is compared with traditional weight-learning methods. The network is better able to function in different scenarios and can recognize multiple stimuli even if it is only trained on single stimuli. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huynh:2008:ijcnn,
author = "Hieu Trung Huynh and Yonggwan Won",
title = "Evolutionary Algorithm for Training Compact Single Hidden Layer Feedforward Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0839.pdf},
url = {},
size = {},
abstract = {An effective training algorithm called extreme learning machine (ELM) has recently proposed for single hidden layer feedforward neural networks (SLFNs). It randomly chooses the input weights and hidden layer biases, and analytically determines the output weights by a simple matrix-inversion operation. This algorithm can achieve good performance at extremely high learning speed. However, it may require a large number of hidden units due to non-optimal input weights and hidden layer biases. In this paper, we propose a new approach, evolutionary least-squares extreme learning machine (ELS-ELM), to determine the input weights and biases of hidden units using the differential evolution algorithm in which the initial generation is generated not by random selection but by a least squares scheme. Experimental results for function approximation show that this approach can obtain good generalization performance with compact networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Turchetti:2008:ijcnn,
author = "Claudio Turchetti and Francesco Gianfelici and Giorgio Biagetti and Paolo Crippa ",
title = "A Computational Intelligence Technique for the Identification of Non-Linear Non-Stationary Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0840.pdf},
url = {},
size = {},
abstract = {This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loève Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functionals, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported. },
keywords = { Non-Linear Non-Stationary System Identification (NLNSSI), Karhunen-Lo`eve Transform (KLT), Statistical Signal Processing, Polynomial Approximation, Volterra Series (VS), Lee-Schetzen Method.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Theera-Umpon:2008:ijcnn,
author = "Nipon Theera-Umpon and Sansanee Auephanwiriyakul and Sitawit Suteepohnwiroj and Kittichai Wantanajittikul",
title = "River Basin Flood Prediction Using Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0841.pdf},
url = {},
size = {},
abstract = {This paper presents a river flood prediction technique using support vector machine (SVM). We investigated the 2-year data covering 2005 and 2006 and 7 crucial river floods that occurred in the downtown of Chiang Mai, Thailand. Past and current river levels of the 3 gauging stations are used as the input data of the SVM models to predict the river levels at the downtown station in 1 hour and 7 hours in advance. The performances of the SVM models are compared with that of the multilayer perceptrons (MLP) models. The experimental results show that the SVM models can perform better than the MLP models. Moreover, the results from the blind test sets demonstrate that the SVM models are appropriate for warning people before flood events. The proposed SVM prediction models are also implemented in a real-world flood warning system. The predicted river levels are accessible to public via a web site, electronic billboards, and warning stations all over the city. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Koskimäki:2008:ijcnn,
author = "Heli Koskimäki and Ilmari Juutilainen and Perttu Laurinen and Juha Roning ",
title = "Two-level Clustering Approach to Training Data Instance Selection: A Case Study For the Steel Industry",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0842.pdf},
url = {},
size = {},
abstract = {Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent selection of training data for model fitting have to be developed. In this article, training instances are selected to fit predictive regression models developed for optimization of the steel manufacturing process settings beforehand, and the selection is approached from a clustering point of view. Because basic k-means clustering was found to consume too much time and memory for the purpose, a new algorithm was developed to divide the data coarsely, after which k-means clustering could be performed. The instances were selected using the cluster structure by weighting more the observations from scattered and separated clusters. The study shows that by using this kind of approach to data set selection, the prediction accuracy of the models will get even better. It was noticed that only a quarter of the data, selected with our approach, could be used to achieve results comparable with a reference case, while the procedure can be easily developed for an actual industrial environment. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang10:2008:ijcnn,
author = "Qing Zhang and Minho Lee",
title = "Emotion Recognition in Natural Scene Images Based on Brain Activity and Gist",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0843.pdf},
url = {},
size = {},
abstract = {Artificial emotion study will be of utmost importance in future artificial intelligence research. In this paper, an emotion understanding system based on brain activity and ''GIST'' is newly proposed to categorize emotions reflected by natural scenes. According to the strong relationship of human emotion and the brain activity, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are used to analyze and classify emotional states stimulated by a natural scene. The ''GIST'' is used to represent the emotional gist of the natural scene. In other words, by taking the way human brain responding to the same stimulus into consideration, a machine will be able to visually extract the emotional features of natural scenes and achieve interaction with a human in terms of emotional sharing. The experimental results show that positive and negative emotions can be distinguished, and a monkey robot head that can share emotion with human subject during watching an image is implemented. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang11:2008:ijcnn,
author = "Jia-Rui Zhang and Shih-Yu Chiu and Leu-Shing Lan ",
title = "Non-Uniqueness of Solutions of 1-Norm Support Vector Classification in Dual Form",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0844.pdf},
url = {},
size = {},
abstract = {Most of previous research efforts on support vector machines (SVMs) were directed toward efficient implementations and practical applications. In this work, we concentrate on a different aspect of SVMs. Specifically, we investigate the non-uniqueness of SVM solutions. The key features of this work include (1) we concentrate on the cases where the dual solutions are not unique, whereas the primal solutions are unique; (2) our test for non- uniqueness can be directly applied to data points without solving the SVC optimization problem, namely, the non-uniqueness information is obtained before any numerical optimization procedure is employed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Thongkam:2008:ijcnn,
author = "Jaree Thongkam and Guandong Xu and Yanchun Zhang",
title = "AdaBoost Algorithm with Random Forests for Predicting Breast Cancer Survivability",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0846.pdf},
url = {},
size = {},
abstract = {In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Molter:2008:ijcnn,
author = "Colin Molter and David Colliaux and Yoko Yamaguchi",
title = "Working Memory and Spontaneous Activity of Cell Assemblies. A Biologically Motivated Computational Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0849.pdf},
url = {},
size = {},
abstract = {Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is strongly associated with the activity of the prefrontal cortex. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Floares:2008:ijcnn,
author = "Alexandru George Floares",
title = "Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0852.pdf},
url = {},
size = {},
abstract = {Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearisation component. Thus, RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms, even when information is missing from the data. It produces systems of ordinary differential equations from experimental or simulated microarray time series data. On simulated data the accuracy and the CPU time were very good. This is due to reducing the reversing of an ordinary differential equations system to that of individual algebraic equations, and to the possibility of incorporating common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks. },
keywords = {genetic algorithms, genetic programming},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tanaka:2008:ijcnn,
author = "Gouhei Tanaka and Kazuyuki Aihara",
title = "Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Functions for Gray-Level Image Reconstruction",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0854.pdf},
url = {},
size = {},
abstract = {The complex-signum function has been widely used as an activation function in complex-valued recurrent neural networks for multistate associative memory. This paper presents two alternative activation functions with circularity. One is the complex-sigmoid function based on a multilevel sigmoid function defined on a circle. The other is a characteristic of a bifurcating neuron represented by a circle map. The performance of the complex-valued neural networks with the two kinds of activation functions is investigated in multistate associative memory tests. In both networks, the connection weights to store the memory patterns are determined by the generalised projection rule. We also demonstrate gray-level image reconstruction as a possible application of the proposed methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chang:2008:ijcnn,
author = "Chuan-Yu Chang and Ming-Feng Tsai and Shao-Jer Chen",
title = "Classification of the Thyroid Nodules Using Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0855.pdf},
url = {},
size = {},
abstract = {Most of the thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in thyroid nodules. A lot of texture extraction methods were used to characterise the thyroid nodules. Accordingly, the thyroid nodules could be classified by the corresponding textural features. In this paper, five support vector machines (SVM) were adopted to select the significant textural features and to classify the nodular lesions of thyroid. Experimental results showed the proposed method classifies the thyroid nodules correctly and efficiently. The comparison results demonstrated that the capability of feature selection of the proposed method was similar to the sequential floating forward selection (SFFS) method. However, the proposed method is faster than the SFFS method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Muslim:2008:ijcnn,
author = "M. Aziz Muslim and Masumi Ishikawa",
title = "Formation of Graph-based Maps for Mobile Robots using Hidden Markov Models",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0856.pdf},
url = {},
size = {},
abstract = {Ambiguity in sensory-motor signals from a mobile robot due mainly to noise and fluctuation makes a deterministic approach unsatisfactory. In this paper, a stochastic approach based-on Hidden Markov Models (HMMs) is proposed to recognize environment of a mobile robot. From this recognition a graph-based map is formed. Graph-based maps are important in decreasing memory and the computational cost. Two methods for constructing graph-based maps are proposed. The former is to estimate HMMs based on quantized sensory-motor signals. The latter is to estimate HMMs based on a sequence of labels obtained by modular network SOM (mnSOM). Although mnSOM learns non-linear dynamics of sensory-motor signals, it still generates labels from each subsequence separately. This might not be robust, because resulting sequence of labels may rapidly change, which rarely occurs in the real world. This motivates us to combine mnSOM and HMM to realize more robust segmentation of the environment. The resulting HMMs can be converted into a graph-based map in a straightforward way. The resulting graph-based map is also useful for goal seeking. Simulation results demonstrate that the proposed method can construct graph-based maps effectively, and can perform goal seeking in the changing environment. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hsieh:2008:ijcnn,
author = "Ji-Lung Hsieh and Chuen-Tsai Sun",
title = "Building a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0857.pdf},
url = {},
size = {},
abstract = {Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in Real-Time Strategy(RTS) Games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agent's decision rules by player's experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tahersima:2008:ijcnn,
author = "Fatemeh Tahersima and Babak Nadjar Araabi",
title = "Approximation of a Map and its Derivatives with an RBF Network Using Input-Output Clustering",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0858.pdf},
url = {},
size = {},
abstract = {Radial Basis Function Networks (RBFNs) are widely used in curve-fitting problems and nonlinear dynamical systems modelling. Using the gradient of the function during the training phase leads to a smooth approximation of both the function itself, and its derivatives. The knowledge about gradient of the function in some identification and control tasks is desired, particularly when the stability and robustness of the system are studied. In this paper, a new clustering based algorithm for learning an Input-Output map along with its derivatives using RBFN is proposed. The input-output clustering (IOC) algorithm for the training of an RBFN is modified to improve the performance of the network in approximating a nonlinear single-input single-output map along with its derivatives using a set of input-output data and the first derivative of the function in each data point. The advantage of the proposed algorithm, in comparison with orthogonal least square (OLS), is demonstrated with an example in the field of data interpolation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Szymański:2008:ijcnn,
author = "Julian Szymański and Włodzisław Duch",
title = "Knowledge Representation and Acquisition for Large-Scale Semantic Memory",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0859.pdf},
url = {},
size = {},
abstract = {Acquisition and representation of semantic concepts is a necessary requirement for the understanding of natural languages by cognitive systems. Word games provide an interesting opportunity for semantic knowledge acquisition that may be used to construct semantic memory. A task-dependent architecture of the knowledge base inspired by psycholinguistic theories of human cognition process is introduced. The core of the system is an algorithm for semantic search using a simplified vector representation of concepts. Based on this algorithm a 20 questions game has been implemented. This implementation provides an example of an application of the semantic memory, but also allows for testing the linguistic competence of the system. A web portal with Haptek-based talking head interface facilitates acquisition of a new knowledge while playing the game and engaging in dialogs with users. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Geng:2008:ijcnn,
author = "Yang Geng and Jongdae Jung and Donggug Seol",
title = "Sound-Source Localization System Based on Neural Network for Mobile Robots",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0860.pdf},
url = {},
size = {},
abstract = {In this paper we described a sound-source localization (SSL) system which can be applied to mobile robot and automatic control systems. A novel approach of using artificial neural network was proposed to obtain the horizontal direction angle (azimuth) of the sound source. According to humanoid characteristic only two microphones, which were attached symmetrically on both sides of the robot as its two ears, were used and tested. Sound wave signals were received from both microphones and analyzed directly by neural network. Two sets of training data were collected and used to train neural network, according to which, different performances of the SSL system were verified and compared. The strong recognizing and calculating ability of neural network made the system work effectively and accurately. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jr.:2008:ijcnn,
author = "Euclides Peres Farias Jr. and Júlio Cesar Nievola ",
title = "Comparative of Data Base Evolution in Rule Association Algorithms in Incremental and Conventional Way",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0861.pdf},
url = {},
size = {},
abstract = {Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: A Priori and ZigZag. Execution time clearly shows the advantage of incremental approaches, but when someone needs accurate results concerning the association rules obtained, the matter should be taken with more caution, because the rules obtained are not necessarily in a relation one-to-one, according to the results obtained. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Guo6:2008:ijcnn,
author = "Yimo Guo and Zhengguang Xu ",
title = "Research on the Cellular Neural Network Template for Translation of Gray-scale Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0864.pdf},
url = {},
size = {},
abstract = {A number of templates for image translation using cellular neural network (CNN) have been proposed before. In this paper, all cases of the 3×3 uncoupled CNN template for translation of gray-scale images are investigated and their functions are discussed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Seno:2008:ijcnn,
author = "Bernardo Dal Seno and Matteo Matteucci",
title = "A Genetic Algorithm for Automatic Feature Extraction in P300 Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0865.pdf},
url = {},
size = {},
abstract = {A Brain-Computer Interface (BCI) is an interface that directly analyzes brain activity to transform user intentions into commands. Many known techniques use the P300 eventrelated potential by extracting relevant features from the EEG signal and feeding those features into a classifier. In these approaches, feature extraction becomes the key point, and doing it by hand can be at the same time cumbersome and suboptimal. In this paper we face the issue of feature extraction by using a genetic algorithm able to retrieve the relevant aspects of the signal to be classified in an automatic fashion. We have applied this algorithm to publicly available data sets (a BCI competition) and data collected in our lab, obtaining with a simple logistic classifier results comparable to the best algorithms in the literature. In addition, the features extracted by the algorithm can be interpreted in terms of signal characteristics that are contributing to the success of classification, giving new insights for brain activity investigation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zurada:2008:ijcnn,
author = "Jacek M. Zurada and Janusz Wojtusiak and Fahmida Chowdhury and Cedric J. Jeannot and Maciej A. Mazurowski",
title = "Computational Intelligence Virtual Community: Framework and Implementation Issues",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0866.pdf},
url = {},
size = {},
abstract = {This paper discusses the framework for virtual collaborative environment for researchers, practitioners, users and learners in the areas of computational intelligence and machine learning (CIML) that is currently developed by our group. It also outlines main features of the community portal under construction that will support communication and sharing of computational resources. In particular, selected aspects of structure of the portal such as common formats of data, models, software, publications and software documentation are discussed. The preliminary portal is available at URL: www.cimlcommunity.org. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jiang5:2008:ijcnn,
author = "Jun Jiang and Horace H S Ip",
title = "Active Learning for the Prediction of Phosphorylation Sites",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0867.pdf},
url = {},
size = {},
abstract = {In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of annotated protein training samples. The result has both conceptual and practical implications in protein prediction: it exploits information inherent in the large scale database of non-annotated protein samples and reduces the amount of manual labor required for protein annotation. To the best of our knowledge, active learning has not been explored in phosphorylation sites prediction. Several active learning strategies: single running mode, batch running mode with sample and support vector diversity, were investigated for phosphorylation sites prediction in this work. Our experiments have shown that active learning with SVM is able to reduce the effort of protein annotation by 6.6percent to 25.7percent to yield similar prediction performance as compared with conventional SVM technique. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ozkaya:2008:ijcnn,
author = "N. Ozkaya and S. Sagiroglu",
title = "Intelligent Face Mask Prediction System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0869.pdf},
url = {},
size = {},
abstract = {Biometric based person identification systems are used to provide alternative solutions for security. Although many approaches and algorithms for biometric recognition techniques have been developed and proposed in the literature, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship between biometric features and we have tried to obtain a biometric feature of a person from another biometric feature of the same person. Consequently, we have designed and introduced a new and intelligent system using a novel approach based on artificial neural networks for generating face masks including eyes, nose and mouth from fingerprints with 0.75 - 3.60 absolute percent errors. Experimental results have demonstrated that it is possible to generate face masks from fingerprints without knowing any information about faces. In addition it is shown that fingerprints and faces are related to each other closely. In spite of the proposed system is initial study and it is still under development, the results are very encouraging and promising. Also proposed work is very important from view of the point that it is a new research area in biometrics. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hamdani:2008:ijcnn,
author = "Tarek M. Hamdani and Adel M. Alimi",
title = "Enhancing the Structure and Parameters of the Centers for BBF Fuzzy Neural Network Classifier Construction Based on Data Structure",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0871.pdf},
url = {},
size = {},
abstract = {This paper aims at presenting different strategies for the construction of Beta Basis Function (BBF) Fuzzy Neural Network. These strategies lead to the determination of the network architecture by determining the structure of the hidden layer and parameters of its centers based on data structure. For that, we use Self Organizing Maps (SOM) clustering to construct a mapped structure of the real training data. By analyzing this structure, we proceed to neuron selection. Data sets were also analyzed with the Fuzzy C-Means (FCM) clustering technique to generate fuzzy membership values presenting fuzzy outputs for our Fuzzy Neural model. We propose to estimate the parameters of Beta Basis Function in order to obtain better data coverage. Experimental results show that the use of the proposed technique produces better results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Docusse:2008:ijcnn,
author = "Tiago A. Docusse and Jullyene R. Furlani and Rodolfo P. Romano and Shi-Huang Chen and Norian Marranghello and Aledir S. Pereira",
title = "Microcalcification Enhancement and Classification on Mammograms using the Wavelet Transform",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0872.pdf},
url = {},
size = {},
abstract = {This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27percent using a region growing algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jung:2008:ijcnn,
author = "Jae-Yoon Jung and Janice I. Glasgow and Stephen H. Scott",
title = "A Hierarchical Ensemble Model for Automated Assessment of Stroke Impairment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0876.pdf},
url = {},
size = {},
abstract = {Assessment of sensory, motor and cognitive function of stroke subjects provide important information to guide patient rehabilitation. As many of the currently used measures are inherently subjective and use course rating scales, here we propose a hierarchical ensemble network that can automatically identify stroke patients and assess their upper limb functionality objectively, based on experimental task data. We compare our neural network ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it significantly outperforms competitors. We also demonstrate that our measure scale is congruent with clinical information, responsive with changes of patients motor function, and reliable in terms of test-retest configuration. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lu5:2008:ijcnn,
author = "Ruei-Shan Lu and Shang-Wu Yu and Yi-Hsien Lin",
title = "The Prediction of Applying Smooth Support Vector Regression and Back Propagation Network in Mutual Fund Performance",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0878.pdf},
url = {},
size = {},
abstract = {This study used Smooth Support Vector Regression and Back Propagation Network as the basic theory in study of the mutual fund performance prediction. This paper used return on performance and return on market to make a comparison, and through the risk values, explored each model's advantages and disadvantages. This study used Taiwan's equity fund as the prediction target, the validation study period was January 2004 to December 2004. The empirical results showed that the SSVR application and BPN application can both increase return on investment, and will receive an even better return in the bull market. In addition, applying SSVR prediction model, in the bear market, will also result in excess return, and reduction of the investors' loss. This study thinks that with Smooth Support Vector Regression model and Back Propagation Networking model respectively, according to different risk preferences of investors, investors can, based on their personal risk preferences, choose a suitable prediction model in order to create the excess return in line with the expectations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Castro:2008:ijcnn,
author = "Ana Paula Abrantes de Castro and Jose Demisio Simões da Silva",
title = "Restoring Images with a Multiscale Neural Network Based Technique",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0880.pdf},
url = {},
size = {},
abstract = {This paper describes a neural network based multiscale image restoration approach using multilayer perceptron neural networks trained with artificially degraded images of gray level co-centered circles. The main goal of the approach is to make the neural network learn inherent space relations of the degraded pixels in restoring the image. In the conducted experiment, the degradation is simulated by filtering the image with a low pass Gaussian filter and adding noise to the pixels at preestablished rates. Degraded image pixels make the input and nondegraded image pixels make the target output for the supervised learning process. The neural network performs an inverse operation by recovering a quasi-nondegraded image in terms of least squared. The main difference of the present approach to existing ones relies on the fact the space relations are taken from different scales, thus providing correlated space data to the neural network. The approach attempts to develop a simple method that provide good restored versions of degraded images, without the need of a priori knowledge or estimation of the possible image degradation causes. The multiscale operation is simulated by considering different window sizes around a pixel. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps used to degrade the artificial image of circles. The neural network restoration results show the proposed approach is promising and may be used in restoration processes with the advantage it does not need a priori knowledge of the degradation causes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wyffels:2008:ijcnn,
author = "Francis Wyffels and Benjamin Schrauwen and David Verstraeten and Dirk Stroobandt ",
title = "Band-Pass Reservoir Computing",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0881.pdf},
url = {},
size = {},
abstract = {Many applications of Reservoir Computing (and other signal processing techniques) have to deal with information processing of signals with multiple time-scales. Classical Reservoir Computing approaches can only cope with multiple frequencies to a limited degree. In this work we investigate reservoirs build of band-pass filter neurons which can be made sensitive to a specified frequency band. We demonstrate that many currently difficult tasks for reservoirs can be handled much better by a band-pass filter reservoir. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pina:2008:ijcnn,
author = "Aloísio Carlos de Pina and Gerson Zaverucha",
title = "Combining Attributes to Improve the Performance of Naive Bayes for Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0882.pdf},
url = {},
size = {},
abstract = {Naive Bayes for Regression (NBR) uses the Naive Bayes methodology to numeric prediction tasks. The main reason for its poor performance is the independence assumption. Although many recent researches try to improve the performance of Naive Bayes by relaxing the independence assumption, none of them can be directly applied to the regression framework. The objective of this work is to present a new approach to improve the results of the NBR algorithm, by combining attributes by means of an auxiliary regression algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kirk:2008:ijcnn,
author = "James S. Kirk ",
title = "Chinese Character Identification by Visual Features Using Self-Organizing Map Sets and Relevance Feedback",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0883.pdf},
url = {},
size = {},
abstract = {Because of its ability to condense a data set in a non-linear, dimension-reducing, topology-preseving way, the self-organizing map (SOM) has proven useful in a wide variety of applications. The Chinese Character Identifier (CCI) uses a set of SOMs along with other natural computation tools to address the problem of identifying an unknown Chinese character by its visual features. By repeatedly presenting small sets of Chinese characters to the user and analyzing which characters are chosen as visually similar to the target character, the system is intended to estimate the visual features upon which the user is presently basing his/her notion of visual similarity. An SOM is then chosen that organizes the universe of characters according to the user's feedback. A simple radial basis function network with basis functions defined in the output space of the selected SOM is used to select a set of characters to present to the user next. The result is a trajectory across the 10-dimensional feature space of the Chinese characters in the direction of the target character. The CCI illustrates the promises and the challenges of using a method of seaching high-dimensional data based on relevance feedback that may be termed ``piecewise topography preservation'' (PTP). This paper discusses the application of PTP to a set of 10-dimensional Chinese character data and explains why certain data sets, exemplified by the Chinese character data, pose a problem for the PTP approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Johansson:2008:ijcnn,
author = "Ulf Johansson and Tuve Lofstrom and Henrik Bostrom",
title = "The Problem with Ranking Ensembles Based on Training or Validation Performance",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0884.pdf},
url = {},
size = {},
abstract = {The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets, shows that there is in general nothing to gain in performance on novel data by choosing an ensemble based on any of the training measures evaluated here. This is despite the fact that the measures evaluated include all the most frequently used; i.e. ensemble training and validation accuracy, base classifier training and validation accuracy, ensemble training and validation AUC and two diversity measures. The main reason is that all ensembles tend to have quite similar performance, unless we deliberately lower the accuracy of the base classifiers. The key consequence is, of course, that a data miner can do no better than picking an ensemble at random. In addition, the results indicate that it is futile to look for an algorithm aimed at optimizing ensemble performance by somehow selecting a subset of available base classifiers. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Nakano2:2008:ijcnn,
author = "M. Nakano and S. Karungaru and S. Tsuge and T.Akashi and Y.Mitsukura and M. Fukumi",
title = "Face Information Processing by Fast Statistical Learning Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0885.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ventresca:2008:ijcnn,
author = "Mario Ventresca and Hamid Reza Tizhoosh",
title = "Numerical Condition of Feedforward Networks with Opposite Transfer Functions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0886.pdf},
url = {},
size = {},
abstract = {Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfer functions on the conditioning of feedforward neural network architectures. The goal is not to discuss a new training algorithm nor error surface geometry, but rather to present characteristics of opposite transfer functions which can be useful for improving existing or to develop new algorithms. Our investigation examines two situations: (1) network initialization, and (2) early stages of the learning process. We provide theoretical motivation for the consideration of opposite transfer functions as a means to improve conditioning during these situations. These theoretical results are validated by experiments on a subset of common benchmark problems. Our results also reveal the potential for opposite transfer functions in other areas of, and related to neural networks. },
keywords = { Numerical condition, ill-conditioning, opposite transfer functions, feedforward.},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Trentin:2008:ijcnn,
author = "Edmondo Trentin and Ernesto Di Iorio",
title = "Classification of Molecular Structures Made Easy",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0888.pdf},
url = {},
size = {},
abstract = {Several problems in bioinformatics and cheminformatics concern the classification of molecules. Relevant instances are automatic cancer detection/classification, machinelearning pathologic prediction, automatic predictive toxicology, etc. Molecules may be represented in terms of graphical structures in a natural way: each node in the graph can be used to represent an atom, whilst the edges of the graph represent the atom-atom bonds. Labels (in the form of real-valued vectors) are associated with nodes and edges in order to express physical and chemical properties of the corresponding atoms and bonds, respectively. These structured data are expected to contain more information than a traditional (flat) feature vector, information that may strengthen the classification capabilities of a machine learner. This paper investigates the application of a novel Bayesian/connectionist classifier to this graphical pattern recognition task. The approach is much simpler than stateof- the-art machine learning paradigms for graphical/relational learning. It relies on the idea of describing the graph in terms of a binary relation. The posterior probability of a class given the relation is estimated as a function of probabilistic quantities modeled with a neural network, trained over individual vertex pairs in the graph. The popular and challenging Mutagenesis dataset is considered for the experimental evaluation. Despite its simplicity, the technique turns out to yield the highest recognition accuracies to date on the complete (friendly + unfriendly) dataset, outperforming complex machines (relational and graph neural nets, kernels for graphs, inductive logic programming techniques, etc.). Some preliminary chemical/biological implications are eventually hypothesized in the light of the results obtained. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(R.:2008:ijcnn,
author = "Diego G. Loyola R. ",
title = "Climatology Databases using Neural Networks: Application to Global Temperature Profiles",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0889.pdf},
url = {},
size = {},
abstract = {Climatology databases containing geophysical parameters such as temperature, precipitation, ozone, surface albedo, cloud information, etc., are widely used in remote sensing, atmospheric, oceanographic, climate research, and operational environmental forecasting communities.Climatology databases are usually constructed as lookup tables with discrete regular latitude, longitude and time grids. The lookup table climatologies not only require large amount of memory, but also retrieving information from the climatology databases can be very time consuming as it usually requires search and interpolation on a multi-dimensional space.This paper presents a neural network approach for creating climatology databases that overcome the problems of the classical lookup tables. The neural networks provide in addition to the output parameters their first-order partial derivatives (Jacobian matrix) required for statistical analysis and retrieval algorithms. Moreover, the neural networks can use new proxies to efficiently fetch data from the climatology; the parameters obtained are therefore closer to the real state of the Earth's system. The proposed approach is applied to the development of a neural network based temperature profile climatology and the results are discussed in detail. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Tsonos:2008:ijcnn,
author = "Dimitrios Tsonos and Kalliopi Ikospentaki and Georgios Kouroupetrolgou",
title = "Towards Modeling of Readers' Emotional State Response for the Automated Annotation of Documents",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0890.pdf},
url = {},
size = {},
abstract = {This work presents an experimental study towards modeling the readers' emotional state as a response to font and typesetting elements of documents presented on a LCD display. Any content and/or domain dependent information was excluded from the document that was tested. An automated computer-based experimental procedure has been followed based on the paper-and-pencil Self Assessment Manikin Test. The typographic elements: font colour, size, type, background colour and the typesetting elements: bold, italics, bold-italics, along with their combinations are studied. The results indicate that the combination of text and background colour has the same impact across languages; the font size has a typical behavior. Readers' Emotional State, induced by the typesetting elements and the font type, probably depends on the current as well as on the previously displayed stimuli. A cognitive-based XML architecture for real-time extraction of readers' emotional state relatively to documents' typographic elements is also presented. The results of this paper can be considered when transforming emotionally annotated documents into acoustic modality. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang12:2008:ijcnn,
author = "Byoung-Tak Zhang ",
title = "Cognitive Learning and the Multimodal Memory Game: Toward Human-Level Machine Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0891.pdf},
url = {},
size = {},
abstract = {Machine learning has made great progress during the last decades and is being deployed in a wide range of applications. However, current machine learning techniques are far from sufficient for achieving human-level intelligence. Here we identify the properties of learners required for human-level intelligence and suggest a new direction of machine learning research, i.e. the cognitive learning approach, that takes into account the recent findings in brain and cognitive sciences. In particular, we suggest two fundamental principles to achieve human-level machine learning: continuity (forming a lifelong memory continuously) and glocality (organizing a plastic structure of localized micromodules connected globally). We then propose a multimodal memory game as a research platform to study cognitive learning architectures and algorithms, where the machine learner and two human players question and answer about the scenes and dialogues after watching the movies. Concrete experimental results are presented to illustrate the usefulness of the game and the cognitive learning framework for studying human-level learning and intelligence. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chinellato:2008:ijcnn,
author = "Eris Chinellato and Beata J. Grzyb and Angel P. del Pobil",
title = "Brain Mechanisms for Robotic Object Pose Estimation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0892.pdf},
url = {},
size = {},
abstract = {Integration of multiple visual cues provides natural systems with superior abilities in dealing with nearby objects. This research is aimed at verifying if robotic systems could also benefit from the merging of different visual cues of the same stimulus. A computational model of stereoscopic and perspective orientation estimators, merged according to different criteria, is implemented on a robotic setup and tested in different conditions. Experimental results suggest that the principle of cue integration can make robot sensory systems more reliable and robust. The same results compared with data from human studies show that the model is able to reproduce some well-known neuropsychological effects. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Gorgônio:2008:ijcnn,
author = "Flavius L. Gorgônio and Jose Alfredo F. Costa",
title = "Parallel Self-Organizing Maps with Application in Clustering Distributed Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0893.pdf},
url = {},
size = {},
abstract = {Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover, current privacy requirements in distributed databases demand algorithms with the ability to process clustering securely. Among the unsupervised neural network models, the selforganizing map (SOM) plays a major role. SOM features include information compression while trying to preserve the topological and metric relationship of the primary data space. This paper presents a strategy for efficient cluster analysis in geographically distributed databases using SOM networks. Local datasets relative to database vertical partitions are applied to distinct maps in order to obtain partial views of the existing clusters. Units of each local map are chosen to represent original data and are sent to a central site, which performs a fusion of the partial results. Experimental results are presented for different datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Songsiri:2008:ijcnn,
author = "Patoomsiri Songsiri and Boonserm Kijsirikul and Thimaporn Phetkaew",
title = "Information-Based Dichotomization: A Method for Multiclass Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0895.pdf},
url = {},
size = {},
abstract = {Approaches for solving a multiclass classification problem by Support Vector Machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some limitations in classification accuracy and evaluation time. This paper proposes a novel method that employs information-based dichotomization for constructing a binary classification tree. Each node of the tree is a binary SVM with the minimum entropy. Our method can reduce the number of binary SVMs used in the classification to the logarithm of the number of classes which is lower than previous methods. The experimental results show that the proposed method takes lower evaluation time while it maintains accuracy compared to other methods.},
keywords = { Information-Based Dichotomization, Multiclass Support Vector Machines, Entropy, },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Silva6:2008:ijcnn,
author = "Luciana L. Silva and Mario L. Tronco and Henrique A. Vian and Giovana Pellinson and Arthur J. V. Porto",
title = "Environment Mapping for Mobile Robots Navigation Using Hierarchical Neural Network and Omnivision",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0896.pdf},
url = {},
size = {},
abstract = {Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Koufakou:2008:ijcnn,
author = "Anna Koufakou and Jimmy Secretan and John Reeder and Michael Georgiopoulos",
title = "Fast Parallel Outlier Detection for Categorical Datasets using MapReduce",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0897.pdf},
url = {},
size = {},
abstract = {Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud. The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a dataset are numerical, usually have a quadratic time complexity with respect to the number of points in the dataset, and quite often they require multiple dataset scans. In this paper, we propose a fast parallel outlier detection strategy based on the Attribute Value Frequency (AVF) approach, a high-speed, scalable outlier detection method for categorical data that is inherently easy to parallelize. Our proposed solution, MR-AVF, is based on the MapReduce paradigm for parallel programming, which offers load balancing and fault tolerance. MR-AVF is particularly simple to develop and it is shown to be highly scalable with respect to the number of cluster nodes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Granger:2008:ijcnn,
author = "Eric Granger and Jean-François Connolly and Robert Sabourin",
title = "A Comparison of Fuzzy ARTMAP and Gaussian ARTMAP Neural Networks for Incremental Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0900.pdf},
url = {},
size = {},
abstract = {Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers - the fuzzy ARTMAP and Gaussian ARTMAP neural networks - are characterize and compared for supervised incremental learning in environments where class distributions are fixed. Their potential for incremental learning of new blocks of training data, after previously been trained, is assessed in terms of generalization error and resource requirements, for several synthetic pattern recognition problems. The advantages and drawbacks of these architectures are discussed for incremental learning with different data block sizes and data set structures. Overall results indicate that Gaussian ARTMAP is the more suitable for incremental learning as it usually provides an error rate that is comparable to that of batch learning for the data sets, and for a wide range of training block sizes. The better performance is a result of the representation of categories as Gaussian distributions, and of using categoryspecific learning rate that decreases during the training process. With all the data sets, the error rate obtained by training through incremental learning is usually significantly higher than through batch learning for fuzzy ARTMAP. Training fuzzy ARTMAP and Gaussian ARTMAP through incremental learning often requires fewer training epochs to converge, and leads to more compact networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Sabo:2008:ijcnn,
author = "Devin Sabo and Xiao-Hua Yu",
title = "A New Pruning Algorithm for Neural Network Dimension Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0901.pdf},
url = {},
size = {},
abstract = {The choice of network dimension is a fundamental issue in neural network applications. An optimal neural network topology not only reduces the computational complexity, but also improves its generalization capacity. In this research, a new pruning algorithm based on cross validation and sensitivity analysis is developed and compared with three existing pruning algorithms on various pattern classification problems. Computer simulation results show the network size can be significantly reduced using this new algorithm while the neural network still maintains satisfactory generalization accuracy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Voichiţa:2008:ijcnn,
author = "Călin Voichiţa and Purvesh Khatri and Sorin Drăghici",
title = "Identifying Uncertainty Regions in Support Vector Machines using Geometric Margin and Convex Hulls",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0902.pdf},
url = {},
size = {},
abstract = {Like most classification techniques, the existing Support Vector Machines (SVM) approaches are challenged to correctly classify their input when the data points are either very close to the decision boundary or very dissimilar from the training data set. In both situations, most classifiers including SVMs will still give a prediction by assigning the test point to one of the classes. However, when a test instance is very close to the decision boundary, the side of the boundary on which the instance lies, and hence the predicted class, will depend in many instances more on the choices of the tuning or training parameters rather than a clear differences in features. Furthermore, if a test instance is substantially different from all instances used during the training, the classical SVM classifiers will still assign it to a class although there is little evidence to support such assignment. In both cases, it is very useful for a classifier to be able to assess its ability to classify a given instance by identifying those regions of the feature space in which the class assignments are less certain. In this paper, we propose two novel approaches based on: i) a geometric uncertainty margin and ii) the convex hulls of the training points in the feature space. Our proposed techniques improve upon the existing SVM-based approaches by adding the ability to identify ``uncertainty'' areas where the assignment of a test instance to a class cannot be guaranteed. We illustrate both the problems and our novel techniques on the Iris data set from the UCI machine learning repository. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bernal-Urbina:2008:ijcnn,
author = "M. Bernal-Urbina and A. Flores-Mendez",
title = "Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0903.pdf},
url = {},
size = {},
abstract = {The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm. },
keywords = {genetic algorithms, genetic programming},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Taylor:2008:ijcnn,
author = "Dennis Taylor and Brett Bojduj and Franz Kurfess",
title = "Towards Using Neural Networks to Perform Object-Oriented Function Approximation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0905.pdf},
url = {},
size = {},
abstract = {Many computational methods are based on the manipulation of entities with internal structure, such as objects, records, or data structures. Most conventional approaches based on neural networks have problems dealing with such structured entities. The algorithms presented in this paper represent a novel approach to neural-symbolic integration that allows for symbolic data in the form of objects to be translated to a scalar representation that can then be used by connectionist systems. We present the implementation of two translation algorithms that aid in performing object-oriented function approximation. We argue that objects provide an abstract representation of data that is well suited for the input and output of neural networks, as well as other statistical learning techniques. By examining the results of a simple sorting example, we illustrate the efficacy of these techniques. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Potter:2008:ijcnn,
author = "Chris Potter and Ganesh K. Venayagamoorthy",
title = "MIMO Beam-Forming with Neural Network Channel Prediction Trained By a Novel PSO-EA-DEPSO Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0906.pdf},
url = {},
size = {},
abstract = {A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kumar2:2008:ijcnn,
author = "Akhilesh Kumar and Finn Tseng and Yan Guo",
title = "Hidden-Markov Model Based Sequential Clustering for Autonomous Diagnostics",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0907.pdf},
url = {},
size = {},
abstract = {Despite considerable advances over the last few decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. The fundamental reason for this being the mismatch between the growing diversity and complexity of machinery and equipment employed in industry and the historical reliance on ``point-solution'' diagnostic systems that necessitate extensive characterization of the failure modes and mechanisms (something very expensive and tedious). While these point solutions have a role to play, in particular for monitoring highly-critical assets, generic yet adaptive solutions, meaning solutions that are flexible and able to learn on-line, could facilitate large-scale deployment of diagnostic and prognostic technology.We present a novel approach for autonomous diagnostics that employs model-based sequential clustering with hidden-Markov models as a means for measuring similarity of timeseries sensor signals. The proposed method has been tested on a CNC machining test-bed outfitted with thrust-force and torque sensors for monitoring drill-bits. Preliminary results revealed the competitive performance of the method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu13:2008:ijcnn,
author = "Qingzhong Liu and Andrew H. Sung",
title = "Steganalysis of Multi-Class JPEG Images Based on Expanded Markov Features and Polynomial Fitting",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0909.pdf},
url = {},
size = {},
abstract = {In this article, based on the Markov approach proposed by shi et al. [1], we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisher's linear discriminant. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(C:2008:ijcnn,
author = "G. Jimenez de la C and Jose A. Ruz-Hernandez and R. Salazar-Mendoza",
title = "Obtaining an Optimal Gas Injection Rate for an Oil Production System via Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0911.pdf},
url = {},
size = {},
abstract = {Using a model-based optimization, a neural network model is developed to calculate the optimal values of gas injection rate and oil rate of a gas lift production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gas lifted wells. For both cases minimizing the objective function the proposed strategy shows the ability of the neural networks to approximate the behavior of an oil production system and to solve optimization problems when a mathematical model is not available. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wei:2008:ijcnn,
author = "Xunkai Wei and Rob Law and Lei Zhang and Yue Feng and Yan Dong and Yinghong Li",
title = "A Fast Coreset Minimum Enclosing Ball Kernel Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0913.pdf},
url = {},
size = {},
abstract = {A fast coreset minimum enclosing ball kernel algorithm was proposed. First, it transfers the kernel methods to a center-constrained minimum enclosing ball problem, and subsequently it trains the kernel methods using the proposed MEB algorithm, and the primal variables of the kernel methods are recovered via KKT conditions. Then, detailed theoretical analysis and rigid proofs of our new algorithm are given. After that, experiments are investigated via using several typical classification datasets from UCI machine learning benchmark datasets. Moreover, performances compared with standard support vector machines are seriously considered. It is concluded that our proposed algorithm owns comparable even superior performances yet with rather fast converging speed in the experiments studied in this paper. Finally, comments about the existing problems and future development directions are discussed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ji:2008:ijcnn,
author = "Zhengping Ji and Xiao Huang and Juyang Weng",
title = "Learning of Sensorimotor Behaviors by a SASE Agent for Vision-based Navigation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0915.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a model to develop robots' covert and overt behaviors by using reinforcement and supervised learning jointly. The covert behaviors are handled by a motivational system, which is achieved through reinforcement learning. The overt behaviors are directly selected by imposing supervised signals. Instead of dealing with problems in controlled environments with a low-dimensional state space, our model is applied for the learning in nonstationary environments. Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) Tree is introduce to be the engine of cognitive mapping. Its balanced coarse-to-fine tree structure guarantees real-time retrieval in self-generated high-dimensional state space. Furthermore, K-Nearest Neighbor strategy is adopted to reduce training time complexity. Visionbased outdoor navigation are used as challenging task examples. In the experiment, the mean square error of heading direction is 0° for re-substitution test and 1.1269° for disjoint test, which allows the robot to drive without a big deviation from the correct path we expected. Compared with IHDR [1], LBIHDR reduced the mean square error by 0.252° and 0.5052°, using re-substitution and disjoint test, respectively. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Asaduzzaman:2008:ijcnn,
author = "Md. Asaduzzaman and Md. Shahjahan and Md. M. Kabir and M. Ohkura and K. Murase",
title = "Generation of Equal Length Patterns from Heterogeneous Patterns for Using in Artificial Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0916.pdf},
url = {},
size = {},
abstract = {A challenging task is to classify Internet customers based on their heterogeneous search histories of shopping in the Internet. The problem is the data pattern itself. Each transition of a customer from one page to the next in purchasing a commodity is considered as an attribute and this is a pair of data. The purchase patterns consist of usually different length for different customers. We cannot classify customers using a neural network (NN) due to these two problems - pair of attribute and unequal lengths of data. Here, we have developed an algorithm that can automatically generate equal length data with non-pair attributes. Finally, we use an unsupervised competitive learning in order to classify them because we do not know how many classes are there. We found that most of the customers belong to single category or class. The results we obtained have a nice agreement with the customer's goal. The goal of all customers is to reach a common target page and to purchase a commodity. Therefore, we can consider that they may belong to the same category or class. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xia:2008:ijcnn,
author = "Bin Xia and He Sun and Bao-Liang Lu",
title = "Multi-view Gender Classification based on Local Gabor Binary Mapping Pattern and Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0917.pdf},
url = {},
size = {},
abstract = {This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Homma:2008:ijcnn,
author = "Noriyasu Homma and Kazuhisa Saito and Tadashi Ishibashi and Zeng-Guang Hou and Ashu M. G. Solo",
title = "Shape Features Extraction from Pulmonary Nodules in X-ray CT Images",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0919.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a new computer aided diagnosis method of pulmonary nodules in X-ray CT images to reduce false positive (FP) rate under high true positive (TP) rate conditions. An essential core of the method is to extract and combine two novel and effective features from the raw CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, a principal component analysis technic and any pattern recognition technics such as neural network approaches can then used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wen:2008:ijcnn,
author = "Guihua Wen and Lijun Jiang and Jun Wen",
title = "Kernel Relative Transformation with Applications to Enhancing Locally Linear Embedding",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0921.pdf},
url = {},
size = {},
abstract = {Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive law, the relative transformation(RT) and kernel relative transformation (KRT) are proposed. They can improve the distinction between data points and inhibit the impact of noise and sparsity of data, which can be then applied to construct the neighborhood graph so as to reduce the short circuit edges, while the embedding is still performed in the original space. Subsequently,another enhanced Hessian Locally Linear Embedding approach is developed with significantly increased performance. The conducted experiments on challenging benchmark data sets validate the proposed approaches. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Altamiranda:2008:ijcnn,
author = "Junior Altamiranda and Jose Aguilar and Luís Hernandez",
title = "Data Mining System for Biochemical Analysis in Experimental Physiology",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0923.pdf},
url = {},
size = {},
abstract = {We develop a Data Mining system to assist with the elucidation by graphical means of the biochemical changes in the brains of rodents. Manual analysis of such experiments is increasingly impractical because of the voluminous nature of the data that is generated, and the tedious nature of the analysis means that important information can be missed. For this purpose we are constructing an increasingly sophisticated Data Mining system which contains a number of pre-processing stages and classification via two steps of an Adaptive Resonance Theory Artificial Neural Network. In this paper we describe the system. The focus of our activity is the study of neurotransmitters: Glutamate and Aspartate and we present an example of how to use our Data Mining system for the automated classification of samples that are extracted from the brains of rodents. This methodology should prove equally valuable to other biochemical analysis problems in experimental Physiology. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li11:2008:ijcnn,
author = "Cuiran Li and Chengshu Li",
title = "Opportunistic Spectrum Access in Cognitive Radio Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0924.pdf},
url = {},
size = {},
abstract = {Radio spectrum is one of the most scare and valuable resources for wireless communications. Cognitive Radio has been considered as an efficient means to opportunistic spectrum sharing between primary (licensed) users and cognitive radio users. In this paper, based on the a two-phase channel and power allocation scheme proposed by A. T. Hoang etc., we present an opportunistic spectrum access approach for cognitive radio network. In the scheme proposed by A. T. Hoang etc., they consider a cognitive radio network that consists of multiple cells and the system throughput is defined as the total number of subscribers that can be simultaneously served. In this paper, we consider a cognitive radio network as self-organizing network. Furthermore, the throughput is defined as the average probability of success transmission. In our proposed approach, for each available channel, TDMA frame consists of N time slots, and each active cognitive user is assigned one transmission slot different from those of other active cognitive users in each frame. It allows an active cognitive user use the slots pre-assigned to the other active cognitive users under a range of values for accessing opportunity. We evaluate the performances of the opportunistic spectrum access approach in view of system throughput and energy efficiency. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mainali:2008:ijcnn,
author = "Manoj Kanta Mainali and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa",
title = "Optimal Route of Road Networks by Dynamic Programming",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0925.pdf},
url = {},
size = {},
abstract = {This paper introduces an iterative Q value updating algorithm based on dynamic programming for searching the optimal route and its optimal traveling time for a given Origin-Destination (OD) pair of road networks. The proposed algorithm finds the optimal route based on the local traveling time information available at each adjacent intersection. For all the intersections of the road network, Q values are introduced for determining the optimal route. When the Q values converge, we can get the optimal route from multiple sources to single destination. If there exist multiple routes with the same traveling time, the proposed method can find all of it. When the traveling time of the road links change, an alternative optimal route is found starting with the already obtained Q values. The proposed method was applied to a grid like road network and the results show that the optimal route can be found in a small number of iterations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu14:2008:ijcnn,
author = "Wenxin Liu and Li Liu and David A. Cartes",
title = "Neural Network Based Controller Design for Three-Phase PWM AC/DC Voltage Source Converters",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0927.pdf},
url = {},
size = {},
abstract = {Three-phase AC/DC converter is widely used in many industrial applications. To improve performance, this paper proposes an adaptive neural network based controller design for three-phase PWM AC/DC voltage source converters. The controller is designed based on a nonlinear multi-input multi-output model using Lyapunov's direct method. Since neural networks can approximate unknown nonlinear dynamics, there is no need to know the parameters of the system. In this way, the controller is robust to parameter drifting and changes of operating points. Additionally, the proposed control can be applied directly online after initialization. Thus, the time-consuming offline training process is avoided. Furthermore, the proposed controller design also avoids the singularity problem, which may exist in regular feedback linearization based controls. Co-simulation using Matlab /Simulink and PSIM demonstrates the effectiveness of the proposed controller design. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Matsumoto:2008:ijcnn,
author = "Yuji Matsumoto and Motohide Umano and Masahiro Inuiguchi",
title = "Visualization with Voronoi Tessellation and Moving Output Units in Self-Organizing Map of the Real-Number System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0928.pdf},
url = {},
size = {},
abstract = {The Self-Organizing map (SOM) proposed by T. Kohonen is a method to produce a low-dimensional representation from high-dimensional input data automatically, where output units are restrictedly placed on grid points. We propose real-number SOM (RSOM), where output units are freely placed on the real-number coordinates plane and visualized as a Voronoi diagram. RSOM is a natural extension of the conventional SOM because Voronoi tessellation for the output units on the square grid generates square regions on the output plane, the same as the conventional SOM. We propose two methods of moving with preserving topology of the input data and several visualization method such as minimum spanning tree, variable boundary width and spherical RSOM. We illustrate moving methods decrease errors in results of simulation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hasegawa:2008:ijcnn,
author = "Tomonari Hasegawa and Yusuke Matsuoka and",
title = "Analysis of Inter-Spike Interval Characteristics of Piecewise Constant Chaotic Spiking Oscillators",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0930.pdf},
url = {},
size = {},
abstract = {This paper studies dynamics of a simple chaotic spiking oscillator having piecewise constant characteristics. The state variable can vibrate and is reset to the base level just after it reaches the threshold. Repeating this vibrate-and-fire behavior, rich chaotic spike-trains can be generated. Since the solution and return map are piecewise linear, precise analysis is possible. We have investigated characteristics of inter-spike intervals (ISIs) and have found interesting properties: ``The system can output chaotic spike-trains characterized by line-like spectrums of ISIs. Such phenomena and chaos with continuous spectrum appear alternately and make window-like structure in the parameter space. The continuous spectrum of chaos can have wider-band than other types of spiking oscillators.'' Presenting a simple electric circuit, typical phenomena are confirmed experimentally. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(He3:2008:ijcnn,
author = "Wenwu He and Hui Jiang",
title = "Explicit Update vs Implicit Update",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0932.pdf},
url = {},
size = {},
abstract = {In this paper, the problem of implicit online learning is considered. A tighter convergence bound is derived, which demonstrates theoretically the feasibility of implicit update for online learning. Then we combine SMD with implicit update technique and the resulting algorithm possesses the inherent stability. Theoretical result is well corroborated by the experiments we performed which also indicate that combining SMD with implicit update technique is another promising way for online learning. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lin3:2008:ijcnn,
author = "Chin-Teng Lin and Nikhil R. Pal and Chien-Yao Chuang and Tzyy-Ping Jung and Li-Wei Ko and Sheng-Fu Liang",
title = "An EEG-based Subject- and Session-Independent Drowsiness Detection",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0933.pdf},
url = {},
size = {},
abstract = {Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data to account for individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subject's cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subject's changing level of alertness, indexed by subject's behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Karnick:2008:ijcnn,
author = "Matthew Karnick and Metin Ahiskali",
title = "Learning Concept Drift in Nonstationary Environments Using an Ensemble of Classifiers Based Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0934.pdf},
url = {},
size = {},
abstract = {We describe an ensemble of classifiers based approach for incrementally learning from new data drawn from a distribution that changes in time, i.e., data obtained from a nonstationary environment. Specifically, we generate a new classifier using each additional dataset that becomes available from the changing environment. The classifiers are combined by a modified weighted majority voting, where the weights are dynamically updated based on the classifiers' current and past performances, as well as their age. This mechanism allows the algorithm to track the changing environment by weighting the most recent and relevant classifiers higher. However, it also uses old classifiers by assigning them appropriate voting weights should a cyclical environment renders them relevant again. The algorithm learns incrementally, i.e., it does not need access to previously used data. The algorithm is also independent of a specific classifier model, and can be used with any classifier that fits the characteristics of the underlying problem. We describe the algorithm, and compare its performance using several classifier models, and on different environments as a function of time for several values of rate-of-change. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Karasuyama:2008:ijcnn,
author = "Masayuki Karasuyama and Ryohei Nakano",
title = "Optimizing Sparse Kernel Ridge Regression Hyperparameters Based on Leave-One-Out Cross-Validation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0935.pdf},
url = {},
size = {},
abstract = {Kernel Ridge Regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter σ.We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian Process. Since the cost of KRR training is O(N3) where N is a data size, to reduce this complexity, some sparse approximation of the KRR is recently studied. In this paper, we apply the minimum crossvalidation (MCV) approach to such sparse approximation. Our experiments show the MCV with the sparse approximation of the KRR can achieve almost the same generalization performance as the MCV-KRR with much lower cost. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shi:2008:ijcnn,
author = "Xuelin Shi and Ying Zhao and Xiangjun Dong",
title = "RDF Based Integrated Information Retrieval in Grid Computing Environment",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0936.pdf},
url = {},
size = {},
abstract = {The information explosion calls for adequate and efficient approaches to information retrieval. Integrated Information Retrieval (IIR) in grid computing environment is becoming more and more attractive for integration and share of heterogeneous resource to provide users integrated retrieval services. This paper proposes IIR service infrastructure on grid platform, GIIRS, which used Resource Description Framework (RDF) as data representation specification. And we designed a query mechanism to implement IIR of heterogeneous and semi- structured web data. The GIIRS can be easily deployed on grid platform and have feature of semantic interoperability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ohta:2008:ijcnn,
author = "Masaya Ohta and Keiichi Mizutani and Naoki Fujita and Katsumi Yamashita ",
title = "Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal and its FPGA Implementation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0938.pdf},
url = {},
size = {},
abstract = {In this paper, a neural network (NN) for peak power reduction of orthogonal frequency-division multiplexing (OFDM) signals is improved in order to suppress its computational complexity. Numerical experiments show that the proposed NN has less computational complexity than the conventional one. The number of IFFT in NN can be reduced to half, and the computational time can be suppressed by 32.7percent. From the HDL simulation for FPGA implementation, hardware resouces are approximately suppressed by about 30percent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kumar3:2008:ijcnn,
author = "Sachin Kumar and Myra Torres and Y. C. Chan and Michael Pecht",
title = "A Hybrid Prognostics Methodology for Electronic Products",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0939.pdf},
url = {},
size = {},
abstract = {Prognostics and health management enables in-situ assessment of a product's performance degradation and deviation from an expected normal operating condition. A unique hybrid prognostics and health management methodology combining both data-driven and physics-of-failure models is proposed for fault diagnosis and life prediction. The shortcomings of using data-driven and physics-of-failure methodologies independently are discussed. These approaches estimate future system health, based on a systems current health status, historical performance, and operating environmental conditions. Although these methodologies are applicable to legacy, current, and future electronics, and ranging from components to circuit assemblies and electronic products, the hybrid approach is preferred due to its capability to include potential failure precursor parameters with failure mechanism, thus improving accuracy in prognostic estimates. Various works on data-driven and physics-of-failure approaches to prognostics for electronics are summarized and a hybrid methodology case study is presented. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alexandrino:2008:ijcnn,
author = "Jose Lima Alexandrino and Cleber Zanchettin and Edson Costa de Barros Carvalho Filho",
title = "A Hybrid Intelligent System Clonart for Short and Mid-term Forecasting for the Brazilian Energy Distribution System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0940.pdf},
url = {},
size = {},
abstract = {The present work describes an application of Clonart (Clonal Adaptive Resonance Theory) for forecasting of amount of precipitation for the Brazilian Energy Distribution System. The effectiveness of the Brazilian electricity system directly depends on the difference between hydroelectric energy production and consumer use. Production depends upon the volume of water stored in the reservoirs. A forecasting system for the amount of rainfall throughout the year contributes significantly to the analysis. The plasticity of the Clonart ensures that a new piece of knowledge does not overshadow previous knowledge. This is especially important for forecast problems because this type of problem needs constants training. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Dominey:2008:ijcnn,
author = "Peter F. Dominey and Isabelle Tapiero and Carol Madden and Emmanuel Reynaud and Michel Hoen and Olivier Koenig ",
title = "A Hybrid Propositional-Embodied Cognitive Architecture for Human-Robot Cooperation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0942.pdf},
url = {},
size = {},
abstract = {Robot platforms have now reached a level of technical development wherein they are becoming physically capable of useful interaction with humans, while ensuring safety and reasonable cost. The current challenge is for cognitive systems science to provide these robots with the necessary capabilities so that they can interact and cooperate with humans in a natural manner. We are addressing this problem by exploiting two central ideas derived from the human psychological sciences. The first idea is that the human conceptual system is based on situated simulations that are instantiated in the same systems that are used for perception and action, referred to as embodied cognition. The second idea is that human cooperation relies on the cooperating agents sharing a common representation of their shared plan, which involves the actions of both agents. This representation allows them to cooperate, to trade roles, and to help one another if necessary. We have implemented these concepts on multiple robot platforms including the HRP2 humanoid, and the Cooperator and Cooperator II visually guided robot manipulators. This paper will present the motivation for this system and results, and will then outline what we consider to be the crucial issues for human-like cognitive systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Taherkhani:2008:ijcnn,
author = "A. Taherkhani and A. Mohammadi and S. A. Seyyedsalehi and H. Davande",
title = "Design of a Chaotic Neural Network by Using Chaotic Nodes and NDRAM Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0944.pdf},
url = {},
size = {},
abstract = {Recent developments in nonlinear dynamics and the theory of chaos have shown deterministic chaotic property of EEGs. Such evidences made the researchers try to take advantage of the chaotic behavior in artificial neural networks. According to the natural selection theory a good problemsolver should have two main properties: The ability of emerging various solutions for problem and existence of a rule (or intelligence) to guide this evolution and variety to become close to the goal. In this paper we used a chaotic node with logistic map to make the ability of emerging various solutions. In order to intelligently control the evolution of chaotic nodes we designed a rule by using NDRAM. The performance of proposed chaotic neural network is about 80percent whereas the performance of NDRAM is about 40percent in the same condition. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pereira:2008:ijcnn,
author = "Cristiano de S. Pereira and George D. C. Cavalcanti",
title = "Prototype Selection: Combining Self-Generating Prototypes and Gaussian Mixtures for Pattern Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0945.pdf},
url = {},
size = {},
abstract = {This paper presents an investigation into prototype-based classifiers. Different methods have been proposed to deal with this problem. There are two main classes of prototype-selection algorithms. The first is merely selective, in which the resulting set of prototypes is formed by wellchosen samples from the training set. The second is known as the creative class of algorithms. This strategy creates new instances and performs adjustments of the prototypes during training. Two methods of the creative strategy are presented here: a self-generating prototype scheme and a fuzzy variation of Nearest Prototype Classification, which uses a Gaussian Mixture ansatz. The respective advantages and problems are discussed. A hybrid method is proposed to overcome difficulties and improve accuracy. The hybrid strategy obtained better results in the experiments when compared to each of two basic approaches and the classic K-Nearest Neighbor. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Puppala:2008:ijcnn,
author = "Hima B. Puppala and Robert Kozma",
title = "Identification of Phase Transitions in Simulated EEG Signals",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0946.pdf},
url = {},
size = {},
abstract = {The KIV model is a biologically inspired hierarchical model that describes non-linear dynamics found in brains. Previous animal and human EEG measurements indicated the presence of jumps in the spatio-temporal EEG patterns, which are relevant to cognitive processing. The present work introduces the KIV model to simulate phase transitions in EEG signals. Phase transitions have nonstationary and intermittent characteristics, which make automated detection a very difficult task. We analyze the simulated EEG signals using various statistical methods. We describe various classification methods to identify simulated phase transitions, which will be used to automate the detection process in actual EEG signals. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Barros:2008:ijcnn,
author = "Adelia C. A. Barros and George D. C. Cavalcanti",
title = "Combining Global Optimization Algorithms with a Simple Adaptive Distance for Feature Selection and Weighting",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0947.pdf},
url = {},
size = {},
abstract = {This work focuses on a study about hybrid optimization techniques for improving feature selection and weighting applications. For this purpose, two global optimization methods were used: Tabu Search (TS) and Simulated Annealing (SA). These methods were combined to k-Nearest Neighbor (k-NN) composing two hybrid approaches: SA/k-NN and TS/k-NN. Those approaches try to use the main advantage from the global optimization methods: they work efficiently in searching for solutions in the global space. In this study, the methodology is proposed by [4]. In the referred work, a hybrid TS/k-NN approach was suggested and successfully applied for feature selection and weighting problems. Based on the later, this analysis indicates a new SA/k-NN combination and compares their results using the classical Euclidean Distance and a Simple Adaptive Distance [8]. The results demonstrate that feature sets optimized by the studied models are very efficient when compared to the well-known k-NN. Both accuracy classification and number of features in the resultant set are considered in the conclusions. Furthermore, the combined use of the Simple Adaptive Distance improves even more the results for all datasets analyzed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kozma:2008:ijcnn,
author = "Robert Kozma and Leonid Perlovsky and JaiSantosh Ankishetty",
title = "Detection of Propagating Phase Gradients in EEG Signals using Model Field Theory of Non-Gaussian Mixtures",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0948.pdf},
url = {},
size = {},
abstract = {Model Field Theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components. The present work uses non-Gaussian components for the description of propagating phase-cones, which are more realistic models of the experimentally observed physiological processes. This work introduces MFT equations for non- Gaussian transient processes, and describes the identification algorithm. The method is demonstrated using simulated phase cone data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kovalerchuk:2008:ijcnn,
author = "Boris Kovalerchuk and Leonid Perlovsky",
title = "Dynamic Logic of Phenomena and Cognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0950.pdf},
url = {},
size = {},
abstract = {Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process is called dynamic logic (DL) of model construction, which mimics processes of the mind and natural evolution. This paper provides a formal description of Phenomena Dynamic Logic (P-DL) and outlines its extension to the Cognitive Dynamic Logic (C-DL). P-DL is presented with its syntactic, reasoning, and semantic parts. Computational complexity issues that motivate this paper are presented using an example of polynomial models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Resconi:2008:ijcnn,
author = "Germano Resconi and Boris Kovalerchuk",
title = "Fusion in Agent -Based Uncertainty Theory and Neural Image of Uncertainty",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0951.pdf},
url = {},
size = {},
abstract = {In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation, the neural network is composed of agents or neurons, which work to implement uncertainty calculus and many valued logics from crisp perceptual input. This idea is closely related to the Dynamic Logic approach and recent cognitive science experimental discoveries. According to this model having crisp perceptual input, brain (1) produces a less certain representation, (2) processes input at this uncertainty level of representation, (3) converts results to the next more certain level of information representation, (4) processes this information and (5) repeats these steps several times until the acceptable level of certainty is reached. To build such model we rely not on the binary logic but on the logic of the uncertainty to obtain the high flexibility and logic adaptation of the described process. This paper presents a concept of the Agent-based Uncertainty Theory (AUT) based on complex fusion of crisp conflicting judgments of agents Communication among agents is modeled by the fusion process in the neural elaboration. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Phienthrakul:2008:ijcnn,
author = "Tanasanee Phienthrakul and Boonserm Kijsirikul ",
title = "Adaptive Stabilized Multi-RBF Kernel for Support Vector Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0953.pdf},
url = {},
size = {},
abstract = {In Support Vector Regression (SVR), kernel functions are used to deal with nonlinear problem by computing the inner product in a higher dimensional feature space. The performance of approximation depends on the chosen kernels. Although the radial basis function (RBF) kernel has been successfully used in many problems, it still has the restriction in some complex problems. In order to obtain a more flexible kernel function, the non-negative weighting linear combination of multiple RBF kernels is used. Then, the evolutionary strategy (ES) is applied for adjusting the parameters of SVR and kernel function. Moreover, the objective function of the ES is carefully designed, by involving a stability of bounded SVR. This leads to improved generalization performances and avoids the overfitting problem. The experimental results show the ability of the proposed method on symmetric mean absolute percentage error (SMAPE) that outperforms the other objective functions and grid search. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang9:2008:ijcnn,
author = "Ming-Der Yang and Boris P.T. Chen and Chang-Shian Chen",
title = "Using Artificial Neural Network for Outflow Estimation in an Ungauged Area",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0955.pdf},
url = {},
size = {},
abstract = {This research employs an artificial neural network with a variable mathematic structure that is capable of simulating a nonlinear structural system. A backpropagation neural network (BPN) is adopted to estimate outflow for an ungauged area by considering temporal distribution of rainfall-runoff and the spatial distribution of watershed environment. The nonlinear relationship among the physiographic factors, precipitation, and outflow of the specific watershed was established to estimate the outflow of the sub-watershed where no flow gauge has been settled. The model was tested at Bei-Shi watershed of Hou-Long River, Taiwan. Three typhoon occurrences were used for model calibration and verification that indicates the model validity and proves the model suitable for estimating the outflow of an ungauged area. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alzate:2008:ijcnn,
author = "Carlos Alzate and Johan A. K. Suykens ",
title = "Sparse Kernel Models for Spectral Clustering Using the Incomplete Cholesky Decomposition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0956.pdf},
url = {},
size = {},
abstract = {A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal and dual model representations allowing the clustering model to be extended to out-of-sample points. The incomplete Cholesky decomposition is used to compute low-rank approximations of a modified affinity matrix derived from the data which contains cluster information. A reduced set method is also presented to compute efficiently the cluster indicators for out-of-sample data. Simulation results with large-scale toy datasets and images show improved performance in terms of computational complexity },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Al-Mamory2:2008:ijcnn,
author = "Safaa O. Al-Mamory and Zhang Hongli and Ayad R. Abbas",
title = "IDS Alarms Reduction Using Data Mining",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0960.pdf},
url = {},
size = {},
abstract = {The Intrusion Detection Systems (IDSs) are one of robust systems which can effectively detect penetrations and attacks. However, they generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this paper, a new approximation algorithm has developed to group alarms and to produce clusters. Hereafter, each cluster abstracted as a generalized alarm; most of the generalized alarms are root causes. The proposed algorithm makes use of nearest neighboring and generalization concepts. As a clustering algorithm, the proposed algorithm uses a new measure to compute distances between alarms features values. This algorithm was verified with many datasets, and its reduction ratio was about 93percent of the total alarms. The resulting generalized alarms help the security analyst in writing filters. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhu2:2008:ijcnn,
author = "Yingying Zhu and Zhong Ming and Jun Zhang",
title = "Video Scene Classification and Segmentation Based on Support Vector Machine",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0961.pdf},
url = {},
size = {},
abstract = {Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on Support Vector Machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse range of genres by allowing sets of features to be combined and compared automatically without the use of thresholds. With the temporal behaviors of different scene classes, SVM classifier can effectively classify presegmented video clips into one of the predefined scene classes. After identifying scene classes, the scene change boundary can be easily detected. The experimental results show that the proposed system not only improves precision and recall, but also performs better than the other classification systems using the decision tree (DT), K Nearest Neighbor (K-NN) and Neural Network (NN). },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mirza:2008:ijcnn,
author = "Hanane H. Mirza and Hien D. Thai and Zensho Nakao",
title = "A New Intelligent Digital Right Management Technique for E-Learning Content",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0962.pdf},
url = {},
size = {},
abstract = {The digitalization of E-learning sources makes it an easy target for frauds, conterfeiting and content stealing. In this paper we present a new technique to deal with the security problems of e-learning content, its authentication and Digital Right Management. The proposed technique is done by inserting a digital logo image, which serves as watermark signals, in the audio stream of E-learning material. This technique is based on Modulated Complex Lapped Transform that was selected for its audio reconstruction properties and the extraction of the watermark is performed using an Independent Component Analysis algorithm. To demonstrate the effectiveness of the proposed method, a real world implementation has been done and the algorithm shows quite good visual and audible quality in watermarked content, as well as a high robustness against common signal processing attacks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ferreira4:2008:ijcnn,
author = "P. M. Ferreira and A. E. Ruano",
title = "Application of Computational Intelligence Methods to Greenhouse Environmental Modelling",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0963.pdf},
url = {},
size = {},
abstract = {In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg- Marquardt optimisation method and their structure is selected by means of multi-objective genetic algorithms. By network structure we refer to the number of neurons of the networks, the input variables and for each variable considered its lagged input terms. Two types of models were identified: process models (greenhouse climate) and external disturbances (external weather). Pseudo-random binary signals were employed to generate control input commands for the greenhouse actuators, in order to build input/output data sets suitable for the process models identification. The final model arrangement consists of four interconnected models, two of which are coupled, providing greenhouse climate and external weather long term predictions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xiao2:2008:ijcnn,
author = "Yang Xiao and Zhiguo Cao and Yi Zheng and Ruicheng Yan",
title = "Multi-sensor Data Fusion Based on Dynamic Fuzzy Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0964.pdf},
url = {},
size = {},
abstract = {In this paper, a multi-sensor data fusion method based on dynamic fuzzy neural network (DFNN) for object recognition is proposed. DFNN is composed of two individual fuzzy neural networks. During the practical recognition process, one fuzzy neural network is used for recognition while the other is tracking trained. At the appropriate time the role of the two networks can be exchanged according to certain switching rule. The fusion recognition system is composed of two layers. At the first layer, the features extracted from middle wave and long wave infrared images are fused by DFNN to detect potential regions which may contain objects. And then the features extracted from visible image are used to make recognition in these potential regions based on DFNN at the second layer. The experiment demonstrates the efficiency of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Isawa:2008:ijcnn,
author = "Haruka Isawa and Haruna Matsushita",
title = "Fuzzy Adaptive Resonance Theory Combining Overlapped Category in Consideration of Connections",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0965.pdf},
url = {},
size = {},
abstract = {Adaptive Resonance Theory (ART) is an unsupervised neural network. Fuzzy ART (FART) is a variation of ART, allows both binary and continuous input patterns. However, Fuzzy ART has the category proliferation problem. In this study, to solve this problem, we propose a new Fuzzy ART algorithm: Fuzzy ART Combining Overlapped Category in consideration of connections (C-FART). C-FART has two important features. One is to make connections between similar categories. The other is to combine overlapping categories into with connections one category. We investigate the behavior of C-FART, and compare C-FART with the conventional FART. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jankowski:2008:ijcnn,
author = "Norbert Jankowski and Krzysztof Grabczewski",
title = "Building Meta-Learning Algorithms Basing on Search Controlled by Machine Complexity",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0966.pdf},
url = {},
size = {},
abstract = {Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Metalearning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored earlier. The learning algorithm is augmented by meta-knowledge repository which accumulates information about progress of the search through the space of candidate solutions. The approach facilitates using human experts knowledge to restrict the search space and provide goal definition, gaining meta-knowledge in an automated manner. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Vallejo:2008:ijcnn,
author = "Jose Refugio Vallejo and Eduardo Bayro-Corrochano",
title = "Clifford Hopfield Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0967.pdf},
url = {},
size = {},
abstract = {This paper presents the properties and the definition of Hopfield Neural Networks as a natural extension to Complex Hopfield Neural Networks and Quaternionic Hopfield Neural Networks. This extension allows us to generalize the concept of Hopfield Neural Networks to all type of Algebras and also to describe the main characteristics of this Networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bayro-Corrochano:2008:ijcnn,
author = "Eduardo Bayro-Corrochano and J. Refugio Vallejo-Gutierrez and Nancy Arana-Daniel",
title = "Recurrent Clifford Support Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0968.pdf},
url = {},
size = {},
abstract = {This paper introduces the Recurrent Clifford Support Vector Machines (RCSVM). First we explain the generalization of the real- and complex- valued Support Vector Machines using the Clifford geometric algebra. In this framework we handle the design of kernels involving the Clifford or geometric product and one redefines the optimization variables as multivectors. This allows us to have a multivector as output therefore we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM to build a recurrent CSVM.We study the performance of the recurrent CSVM with experiments using time series and tasks of visually guided robotics. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Monwar:2008:ijcnn,
author = "M. M. Monwar and S. Rezaei",
title = "Video Analysis for View-Based Painful Expression Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0969.pdf},
url = {},
size = {},
abstract = {In recent years, facial expressions of pain have been the focus of considerable behavioral research. Such work has documented that pain expressions, like other affective facial expressions, play an important role in social communication. Enabling computer systems to recognize pain expression from facial images is a challenging research topic. In this paper, we present two systems for pain recognition from video sequences. The first approach, eigenimage, projects the face images, detected from video sequences, onto a feature space, defined by eigenfaces, to produce the biometric template. Recognition is performed by projecting a new image onto that feature space and then classifying the face by comparing its position in the feature spaces with the positions of known individuals. To ensure better accuracy, the system is tested against two more feature spaces defined by eigeneyes and eigenlips. The second approach, neural network, extracts location and shape features of the detected faces and uses them as inputs to the artificial neural network which employs the standard error backpropagation algorithm for classification of faces. From experiments, we conclude that neural network based method is better in terms of speed and accuracy than eigenimage based method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qiu3:2008:ijcnn,
author = "Hai Qiu and Neil Eklund and Xiao Hu and Weizhong Yan and Naresh Iyer ",
title = "Anomaly Detection using Data Clustering and Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0971.pdf},
url = {},
size = {},
abstract = {Anomaly detection provides an early warning of unusual behavior in units in a fleet operating in a dynamic environment by learning system characteristics from normal operational data and flagging any unanticipated or unseen patterns. For a complex system such as an aircraft engine, normal operation might consist of multiple modes in a high dimensional space. Therefore, anomaly detection approaches based on single cluster data structure will not work. This paper investigates data clustering and neural network based approaches for anomaly detection, specifically addressing the situation which normal operation might exhibit multiple hidden modes. Results show detection accuracy can be improved by data clustering or learning the data structure using neural networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Peterson:2008:ijcnn,
author = "Leif E. Peterson and Matthew A. Coleman",
title = "Text-Mining Protein-Protein Interaction Corpus using Concept Clustering to Identify Intermittency",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0973.pdf},
url = {},
size = {},
abstract = {We used human protein-protein interaction (PPI) data transformed into documents to perform text-mining via concept clusters. The advantage of text-mining PPI data is that words (proteins) that are very sparse or over-abundant can be dropped, leaving the remaining bulk of data for clustering and rule mining. Libraries of tissue-specific binary PPIs were constructed from a list of 36,137 binary PPIs in the Human Protein Reference Database (HPRD). A randomization test for intermittency in the form of spikes and holes in frequency distributions of cluster-specific word frequencies was developed using scaled factorial moments. The test was based on a permutation form of a log-linear regression model to determine differences in slopes for ln(F2) vs. ln(M) in the intermittent and null distributions. Significant intermittency (p < 0.0005) in PPI was detected for prostate and testis tissue after a Bonferroni adjustment for multiple tests. The presence of intermittency reflects spikes and holes in histograms of cluster-specific word frequencies and possibly suggests identification of novel large signal transduction pathways or networks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ye2:2008:ijcnn,
author = "Zhengmao Ye and Habib Mohamadian",
title = "Independent Component Analysis for Spatial Object Recognition with Applications of Information Theory Synthesis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0975.pdf},
url = {},
size = {},
abstract = {Each moving object contains particular unique signatures that can be used for pattern classification via object recognition and identification. Information extracted from the spatial object feature recognition can be provided by independent basis functions to represent actual physical attributes of the moving objects. Compared with principal component analysis, independent component analysis is a special feature extraction approach for blind signal separation, where an object is labeled to a special class. Some underlying factors or sources can be captured in a statistical sense. The true colour image is composed of red, green and blue components which are perpendicular to each other. These components may serve as a basis to be synthesized using independent component analysis. Each individual signature indicates unique information that can be evaluated using information theory. Thus, the quantitative measures of the colour component energy, discrete entropy and relative entropy have been introduced to independent component analysis issues for recognition of moving objects. },
keywords = { Independent Component Analysis, Discrete Entropy, , Relative Entropy, Colour Component Energy Recognition, Object},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ye3:2008:ijcnn,
author = "Zhengmao Ye and Habib Mohamadian and Yongmao Ye",
title = "Sensing Data Discrete Wavelet Fusion for Pattern Recognition with Qualitative and Quantitative Measuring",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0976.pdf},
url = {},
size = {},
abstract = {Sensing data fusion has various types of real world applications in fields of weather forecasting, environmental surveillance, medical diagnosis, information assurance, space exploration and national security. Image fusion acts as a primary approach of data fusion. For similar images, some unique patterns occur within each individual one. There are some typical image fusion techniques, either area based or feature based. The feature-based approach is efficient and robust to handle multi-sensor image fusion with little rotation or translation, or the image has to be aligned beforehand. The area-based approach has no strict requirement on rotation or translation, but lack of robustness. A combination of two approaches is thus required. In this article, wavelet fusion is presented to analyze the effect of image fusion. Except for qualitative measures, quantitative measures are also proposed to evaluate image fusion. In particular, 2D discrete wavelet transform is used to both decompose images and reconstruct original images using the approximation, horizontal detail, vertical detail and diagonal detail components from the input images. At the same time, quantitative measures are used to evaluate the quality of the 2D wavelet transform and wavelet fusion, where gray level energy, discrete entropy and relative entropy and mutual information are applied. },
keywords = { Pattern Recognition, Gray Level Energy, Discrete Entropy, Relative Entropy, Wavelet Transform, Wavelet Fusion},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hell:2008:ijcnn,
author = "Michel Hell and Pyramo Costa and Fernando Gomide",
title = "Hybrid Neurofuzzy Computing with Nullneurons",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0977.pdf},
url = {},
size = {},
abstract = {In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and neuron and if u = 1, then the nullneuron becomes a dual or neuron. Also, we introduce a new learning scheme for hybrid neurofuzzy networks based on nullneurons using reinforcement learning. This learning scheme adjusts the weights associated with the individual inputs of the nullneurons, and learns the role of the nullneuron in the network (and or or) by individually adjusting the parameter u of each nullneuron. Nullneuron-based neural networks and the associated learning scheme is more general than similar neurofuzzy networks because they allow different triangular norms in the same network structure. Experimental results show that nullneuron-based networks provide accurate results with low computational effort. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lee5:2008:ijcnn,
author = "Jie-Hung Lee and Chiu-Ching Tuan and Tzung-Pei Hong",
title = "A Maximum Channel Reuse Scheme with Hopfield Neural Network-Based Static Cellular Radio Channel Allocation Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0979.pdf},
url = {},
size = {},
abstract = {In recent years, wireless and mobile communication systems become increasingly popular. The demand for mobile communication has thus made the industry put more efforts towards designing new-generation systems. One of the important issues in mobile-phone communications is about the static channel assignment problem (SCAP). Although many techniques have been proposed for SCAP, a challenge for the cellular radio communication system is how to enhance and maximize the frequency reuse. The general SCAP is known as an NP-hard problem. The static channel assignment scheme based on the Hopfield Neural Network was shown to perform well when compared to some other schemes such as graph colouring and genetic algorithm (GA). In this paper, we extend Kim et al.'s modified Hopfield Neural Network methods and focus on channel reusing to obtain a near-optimum solution for CAP. Several constraints are considered for obtaining the desired results. Eight-benchmark problems are simulated and the energy evolution process is discussed. Simulation results demonstrated that the proposed scheme could make higher channel reuse rate. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jin4:2008:ijcnn,
author = "Xu Jin and Habib Abdulrab and Mhamed Itmi",
title = "A Multi-agent Based Model for Urban Demand-Responsive Passenger Transport Services",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0981.pdf},
url = {},
size = {},
abstract = {Multi-agent simulation has been looked as an efficient tool for urban dynamic traffic services. However, the main problem is how to build an agent-based model for it. This research presents a multi-agent based demand responsive transport (DRT) services model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, travel time, waiting time, client's special requests, and using minimum number of vehicle. In this paper, we propose an agent based multi-layer distributed hybrid planning model for the real-time problem which can solve this question. In the proposed method, an agent for each vehicle finds a set of routes by its local search, and selects a route by cooperation with other agents in its planning domain. By computational experiments, we examine the effectiveness of the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Alippi:2008:ijcnn,
author = "C. Alippi and M. Fuhrman and M. Roveri",
title = "k-NN Classifiers: Investigating the k=k(n) Relationship",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0982.pdf},
url = {},
size = {},
abstract = {The paper proposes a theory-based method for estimating the optimal value of k in k-NN classifiers based on a n-sized training set. As expected, experiments show that the suggested k is such that k/n → 0 when both k and n tend to infinity, as required by the asymptotical consistency condition. Interestingly, it appears that the generalization error is robust w.r.t. to k when n becomes large (probably as a consequence of the k/n → 0 relationship); the immediate consequence is that there is no need to provide an accurate estimate for the optimal k and an approximated coarser value, e.g., provided with cross validation, l-fold cross validation or leave one out is more than adequate. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang11:2008:ijcnn,
author = "Wenwu Wang ",
title = "Convolutive Non-Negative Sparse Coding",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0983.pdf},
url = {},
size = {},
abstract = {Non-negative sparse coding (NSC) is a powerful technique for low-rank data approximation, and has found several successful applications in signal processing. However, the temporal dependency, which is a vital clue for many realistic signals, has not been taken into account in its conventional model. In this paper, we propose a general framework, i.e., convolutive non-negative sparse coding (CNSC), by considering a convolutive model for the low-rank approximation of the original data. Using this model, we have developed an effective learning algorithm based on the multiplicative adaptation of the reconstruction error function defined by the squared Euclidean distance. The proposed algorithm is applied to the separation of music audio objects in the magnitude spectrum domain. Interesting numerical results are provided to demonstrate its advantages over both the conventional NSC and an existing convolutive coding method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Herzog:2008:ijcnn,
author = "Andreas Herzog and Karsten Kube and Bernd Michaelis and Thomas Baltz and Thomas Voigt ",
title = "Transmission of Spatio-Temporal Patterns from Biological to Artificial Neural Networks by a Multi-Electrode Array",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0984.pdf},
url = {},
size = {},
abstract = {The monitoring of a set of individual neurons in cultured biological networks or in the brain has become feasible with the used/development of multi-electrode arrays (MEA). However, even with the huge mass of data, getting suitable information about the actual spatio-temporal context of the analyzed biological network is not easy. In this paper we present a new conception and first results of analyzing the measured data by a recurrent artificial neural network with similar parameters as the biological network. The signals of the biological network transfer into the artificial one and the balanced artificial network becomes a part of the dynamics of the biological network. The artificial network is more transparent for advanced methods to analyze synchronous firing patterns (i.e., polychronization) and may also generate adequate feedback signals to the biological network for using as a recurrent neurointerface. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Seiffertt:2008:ijcnn,
author = "John Seiffertt and Donald C. Wunsch II",
title = "A Quantum Calculus Formulation of Dynamic Programming and Ordered Derivatives",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0985.pdf},
url = {},
size = {},
abstract = {Much recent research activity has focused on the theory and application of quantum calculus. This branch of mathematics continues to find new and useful applications and there is much promise left for investigation into this field. We present a formulation of dynamic programming grounded in the quantum calculus. Our results include the standard dynamic programming induction algorithm which can be interpreted as the Hamilton-Jacobi-Bellman equation in the quantum calculus. Furthermore, we show that approximate dynamic programming in quantum calculus is tenable by laying the groundwork for the backpropagation algorithm common in neural network training. In particular, we prove that the chain rule for ordered derivatives, fundamental to backpropagation, is valid in quantum calculus. In doing this we have connected two major fields of research. },
keywords = { dynamic programming, quantum calculus, time scales, backpropagation, dynamic equations},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Coyle:2008:ijcnn,
author = "Damien Coyle and Thomas M. McGinnity and irijesh Prasad ",
title = "A Multi-Class Brain-Computer Interface with SOFNN-based Prediction Preprocessing",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0986.pdf},
url = {},
size = {},
abstract = {Recent research has shown that neural networks (NNs) or self-organizing fuzzy NNs (SOFNNs) can enhance the separability of motor imagery altered electroencephalogram (EEG) for brain-computer interface (BCI) systems. This is achieved via the neural-time-series-prediction-preprocessing (NTSPP) framework where SOFNN prediction models are trained to specialize in predicting the EEG time-series recorded from different EEG channels whilst subjects perform various mental tasks. Features are extracted from the predicted signals produced by the SOFNN and it has been shown that these features are easier to classify than those extracted from the original EEG. Previous work was based on a two class BCI. This paper presents an analysis of the NTSPP framework when extended to operate in a multiclass BCI system. In mutliclass systems normally multiple EEG channels are used and a significant amount of subject-specific parameters and EEG channels are investigated. This paper demonstrates how the SOFNN-based NTSPP, tested in conjunction with three different feature extraction procedures and different linear discriminant and support vector machine (SVM) classifiers, is effective in improving the performance of a multiclass BCI system, even with a low number of standardly positioned electrodes and no subject-specific parameter tuning. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Schwabe:2008:ijcnn,
author = "Lars Schwabe and Olaf Blanke ",
title = "Out-of-Body Experiences: False Climbs in a Supine Position?",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0987.pdf},
url = {},
size = {},
abstract = {Out-of-body experiences (OBEs) are illusions, where people experience themselves as being located outside their physical body and often flying or floating at an elevated location. Here, we propose that the flying and floating in OBEs can be explained as the result of a Bayesian inference, where ambiguous bottom-up signals from the otholiths in a supine position are integrated with a top-down prior for the upright position, which is not appropriate for the current supine position. We also measure these ecologically valid priors for the upright position as the empirical probabilities in natural head movements. Our results suggest a simple interpretation of some aspects of OBEs in terms of a mislead sensory inference and suggests new ways of experimentally inducing OBE-like experiences by manipulating sensory signals and top-down prior information. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kuremoto:2008:ijcnn,
author = "T. Kuremoto and M. Obayashi and K. Kobayashi and H. Adachi and K. Yoneda ",
title = "A Reinforcement Learning System for Swarm Behaviors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0990.pdf},
url = {},
size = {},
abstract = {This paper proposes a neuro-fuzzy system with a reinforcement learning algorithm to realize speedy acquisition of optimal swarm behaviors. The proposed system is constructed with a part of input states classification by the fuzzy net and a part of optimal behavior learning network adopting the actor-critic method. The membership functions and fuzzy rules in the fuzzy net are adaptively formed online by the change of environment states observed in trials of agent's behaviors. The weights of connections between the fuzzy net and the value functions of actor and critic are trained by temporal difference error (TD error). Computer simulations applied to a goal-directed navigation problem using multiple agents were performed. Effectiveness of the proposed learning system was confirmed by the simulation results. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kim4:2008:ijcnn,
author = "Jaekwang Kim and Jee-Hyong Lee",
title = "A Methodology for Finding Source-level Vulnerabilities of the Linux Kernel Variables",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0991.pdf},
url = {},
size = {},
abstract = {Linux kernel provides several advantages to system developers and is widely used as an operating system in a variety of systems, including embedded systems, access routers and servers. These advantages are due to the fact that the Linux kernel is publicly available, however, this feature of openness can have negative impacts on system security. If an attacker wished to exploit Linux-based systems, the attacker could easily do so by finding and abusing the vulnerabilities of the systems' Linux kernel sources. There are several methods available that can find source-level vulnerabilities, but they are not always suitable for the Linux kernel. In this paper, we propose a two-step Onion mechanism as a methodology to find source-level vulnerabilities of the Linux kernel variables. The first step of the Onion mechanism is to select variables that may be vulnerable by exploiting their usage patterns. The second step is to inspect the vulnerabilities of the selected variables by making and analyzing system call trees. We also evaluate our proposed methodology by applying it to two well-known source-level vulnerabilities. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mahdaviani:2008:ijcnn,
author = "Kaveh Mahdaviani and Helga Mazyar and Saeed Majidi and Mohammad H. Saraee",
title = "A Method to Resolve the Overfitting Problem in Recurrent Neural Networks for Prediction of Complex Systems' Behavior",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0992.pdf},
url = {},
size = {},
abstract = {In this paper a new method to resolve the overfitting problem for predicting complex systems' behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks. Finally the results of using this method on two sample datasets are presented and the effectiveness of this method is illustrated. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Souto3:2008:ijcnn,
author = "Marcilio C. P. de Souto and Ricardo B. C. Prudêncio and Rodrigo G. F. Soares and Daniel S. A. de Araujo",
title = "Ranking and Selecting Clustering Algorithms Using a Meta-Learning Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0993.pdf},
url = {},
size = {},
abstract = {We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Santos:2008:ijcnn,
author = "Sergio P. Santos and Jose Alfredo F. Costa",
title = "Application of Multiple Decision Trees for Condition Monitoring in Induction Motors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0994.pdf},
url = {},
size = {},
abstract = {Induction machines (IMs) play a pivotal role in industry and there is a strong demand for their reliable and safe operation. IMs are susceptible to problems such as stator current imbalance and broken bars, usually detected when the equipment is already broken, and sometimes after irreversible damage has occurred. Condition monitoring can significantly reduce maintenance costs and the risk of unexpected failures through the early detection of potential risks. Several techniques are used to classify the condition of machines. This paper presents a new case study on the application of multiple decision trees in the on-line condition monitoring of induction motors. Some advantages can be seen, such as the improved performance of classification systems, in addition to the capacity to explain examples. The database was developed through a simplified mathematical model of the machine, considering the effects caused by asymmetries in the phase impedances of motors. A comparative analysis is performed for individual running (based on the neural networks, k-Nearest neighbor and Naïve Bayes) and a multi-classifier (based on the Bagging and Adaboost) approaches. Results demonstrate that the multi-classifier systems obtain better results than those of the individual experiments. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Frolov:2008:ijcnn,
author = "Alexander Frolov and Dusan Husek and Hana Rezankova and Pavel Polyakov",
title = "Clustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0995.pdf},
url = {},
size = {},
abstract = {In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis [3], hierarchical clustering, and a linear factor analysis on the mushroom dataset [9]. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Caiuta:2008:ijcnn,
author = "Rafael Caiuta and Aurora Pozo and Leonardo Emmendorfer and Silvia Regina Vergilio",
title = "Selecting Software Reliability Models with a Neural Network Meta Classifier",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0997.pdf},
url = {},
size = {},
abstract = {Software reliability is one of the most important quality characteristics for almost all systems. The use of a software reliability model to estimate and predict the system reliability level is fundamental to ensure software quality. However, the selection of an appropriate model for a specific case can be very difficult for project managers. This is because, there are several models that can be used and none has proved to perform well considering different projects and databases. Each model is valid only if its assumptions are satisfied. To aim at the task of choosing the best software reliability model for a dataset, this paper presents a meta-learning approach and describes experimental results from the use of a neural network meta classifier for selection among different kind of reliability models. The obtained results validate the idea and are very promising. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Murphey:2008:ijcnn,
author = "Yi L. Murphey and ZhiHang Chen and Leo Kiliaris and Jungme Park and Abul Masrur and Anthony Phillips",
title = "Neural Learning of Driving Environment Prediction for Vehicle Power Management",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0998.pdf},
url = {},
size = {},
abstract = {Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment such as road types and traffic congestions. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Barnes:2008:ijcnn,
author = "Anna Barnes and Garry Honey and Alle-Meije Wink and John Suckling",
title = "Modulation of the Fractal Properties of Low Frequency Endogenous Brain Oscillations in Functional MRI by a Working Memory Task",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN0999.pdf},
url = {},
size = {},
abstract = {Fractals - signals that display scaleinvariant behaviour - are ubiquitous in nature including a wide variety of physiological processes. Fractal analysis of blood oxygen level dependent (BOLD) time-series of fMRI acquisitions from the brain can be achieved by decomposing the data into a hierarchy of temporal scales so that although the signal may well be irregular and contain singularities, the properties of these singularities are constant in time and the entire series can be characterised by a single scaling exponent: the Hurst exponent, H. The observation that a signal has a noninteger fractal dimension suggests that the generating system is complex and has the potential to adapt to a wide variety of challenges. In contrast, the emergence of white noise or, alternatively, signal periodicity can be seen as degradation of fractal complexity and hence, maladaptivity. We tested the hypothesis that exogenous stimuli affects fractal signal properties in the context of brain function by manipulating the cognitive demand of a working memory task and using H as a summary measure of signal complexity. We show that this stimulus has a significant effect on H estimated from resting data acquired immediately before and after the task, and that the degree of change is related to cognitive load. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li12:2008:ijcnn,
author = "Jianwu Li and Zhanyong Xiao and Yao Lu ",
title = "Adapting Radial Basis Function Neural Networks for One-Class Classification",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1000.pdf},
url = {},
size = {},
abstract = {One-class classification (OCC) is to describe one class of objects, called target objects, and discriminate them from all other possible patterns. In this paper, we propose to adapt radial basis function neural networks (RBFNNs) for OCC. First, target objects are mapped into a feature space by using neurons in the hidden layer of the RBFNNs. Then, we perform support vector domain description (SVDD) with linear kernel functions in the feature space to realize OCC. In addition, we also model, in the feature space, the closed sphere centered on the mean of target objects for OCC. Compared to the SVDD with nonlinear kernel functions, our methods can use flexible nonlinear mappings, which do not necessarily satisfy Mercer's conditions. Moreover, we can also control the complexity of solutions easily by setting the number of neurons in the hidden layer of RBFNNs. Experimental results show that the classification accuracies of our methods can be close to, and even can reach those of the SVDD for most of results, but with typically much sparser models. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Teng:2008:ijcnn,
author = "Teck-Hou Teng and Zhong-Ming Tan and Ah-Hwee Tan ",
title = "Self-Organizing Neural Models Integrating Rules and Reinforcement Learning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1002.pdf},
url = {},
size = {},
abstract = {Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as Fusion Architecture for Learning, COgnition and Navigation(FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated based on pre-existing know-how and inserted into FALCON as a priori knowledge. The availability of knowledge enables FALCON to start performing earlier in the initial learning trials. Through a temporal-difference (TD) learning method, the inserted rules can be refined and expanded according to the evaluative feedback signals received from the environment. Our experimental results based on a minefield navigation task have shown that FALCON is able to learn much faster and attain a higher level of performance earlier when inserted with the appropriate a priori knowledge. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang10:2008:ijcnn,
author = "Sheng-Chih Yang and Yi-Jhen Lin and Pau-Choo Chung and Giu-Cheng Hsu and Chien-Shen Lo",
title = "Mass Screening and Feature Reserved Compression in a Computer-aided System for Mammograms",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1003.pdf},
url = {},
size = {},
abstract = {This paper presents a computer-aided prescreening and storage system, which automatically prescreens the mass regions from mammograms and based on the results, performs a progressive compression in the storage. This is performed in two subsystems called mass screening subsystem and mass feature reserved compression subsystem. In the first subsystem, breast region is firstly extracted from images, followed by Gradient Enhancement and Median Filtering. Then, 19 texture features are calculated from 32*32 pixel blocks on the extracted breast region, and suboptimal feature subset is extracted. Then SVM classifier is employed for classifying the regions into mass, breast without masses and background.In the second subsystem, Vector Quantization GHNN (Grey-based Competitive Hopfield neural network) is applied on the three regions with different compression rates according their importance factors so as to reserve important features and simultaneously reduce the size of mammograms for storage efficiency. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yeh:2008:ijcnn,
author = "Flora Yu-Hui Yeh and Marcus Gallagher",
title = "An Empirical Study of the Sample Size Variability of Optimal Active Learning Using Gaussian Process Regression",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1007.pdf},
url = {},
size = {},
abstract = {Optimal Active Learning refers to a framework where the learner actively selects data points to be added to its training set in a statistically optimal way. Under the assumption of log-loss, optimal active learning can be implemented in a relatively simple and efficient manner for regression problems using Gaussian Processes. However (to date), there has been little attempt to study the experimental behavior and performance of this technique.In this paper, we present a detailed empirical evaluation of optimal active learning using Gaussian Processes across a set of seven regression problems from the DELVE repository. In particular, we examine the evaluation of optimal active learning compared to making random queries and the impact of experimental factors such as the size and construction of the different sub-datasets used as part of training and testing the models. It is shown that the multiple sources of variability can be quite significant and suggests that more care needs to be taken in the evaluation of active learning algorithms. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen13:2008:ijcnn,
author = "Zaiping Chen and Yueming Zhao and Yang Zheng and Rui Lou",
title = "Neural Network Electrical Machine Faults Diagnosis Based on Multi-population GA",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1009.pdf},
url = {},
size = {},
abstract = {A hybrid method combining artificial neural network (ANN) with genetic algorithm (GA) is discussed in this paper. A new strategy of optimization on ANN structure and weights based on multi-population GA is proposed, and the quantitative optimization of ANN is realized. The Levenberg-Marquardt(LM) algorithm is used for further training the neural network, which can avoid the weak local searching ability of GA and shows both of the merits of GA as well as ANN. In this paper, the algorithm proposed is employed in the electrical machine fault diagnosis, and the simulation results verified the correctness and effectiveness of the scheme proposed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Miao2:2008:ijcnn,
author = "Jun Miao and Lijuan Duan and Laiyun Qing and Xilin Chen and Wen Gao",
title = "Visual Context Representation using a Combination of Feature-driven and Object-driven Mechanisms",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1011.pdf},
url = {},
size = {},
abstract = {Visual context between objects is an important cue for object position perception. How to effectively represent the visual context is a key issue to study. Some past work introduced task-driven methods for object perception, which led a large coding quantity. This paper proposes an approach that incorporates feature-driven mechanism into object-driven context representation for object locating. As an example, the paper discusses how a neuronal network encodes the visual context between feature salient regions and human eye centers with as little coding quantity as possible. A group of experiments on efficiency of visual context coding and object searching are analyzed and discussed, which show that the proposed method decreases the coding quantity and improve the object searching accuracy effectively. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Graham:2008:ijcnn,
author = "James Graham and Janusz A. Starzyk",
title = "A Hybrid Self-Organizing Neural Gas Based Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1013.pdf},
url = {},
size = {},
abstract = {This paper examines the neural gas networks proposed by Martinetz and Schulten [1] and Fritzke [2] in an effort to create a more biologically plausible hybrid version. The hybrid algorithm proposed in this work retains most of the advantages of the Growing Neural Gas (GNG) algorithm while adapting a reduced parameter and more biologically plausible design. It retains the ability to place nodes where needed, as in the GNG algorithm, without actually having to introduce new nodes. Also, by removing the weight and error adjusting parameters, the guesswork required to determine parameters is eliminated. When compared to Fritzke's algorithm, the hybrid algorithm performs admirably in terms of the quality of results it is slightly slower due to the greater computational overhead. However, it is more biologically feasible and somewhat more flexible due to its hybrid nature and lack of reliance on adjustment parameters. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(An:2008:ijcnn,
author = "Jing An and Qi Kang and Lei Wang and Qidi Wu",
title = "A Turbo Codes Optimization Method Using Particle Swarm Algorithm",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1014.pdf},
url = {},
size = {},
abstract = {Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good interleaver for the large frame sizes, as well as design the decoding optimization mode (PSO-Turbo); and apply the proposed PSO-Turbo codes mode to the security radio data transmission; in which, a kind of transport control proposal based on PSO-Turbo optimizer for CBTC wireless channel is designed and simulated to validate our method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shi2:2008:ijcnn,
author = "Min Shi and Haifeng Wu and Hasan Fleyeh",
title = "Support Vector Machines for Traffic Signs Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1015.pdf},
url = {},
size = {},
abstract = {In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing the data to the SVM for training and test. We compared and analyzed the performances of the SVM recognition model using different feature representations and different kernels and SVM types. Our experimental data sets consisted of 350 traffic sign shapes and 250 speed limit signs. Experimental results have shown excellent results, which have achieved 100percent accuracy on sign shapes classification and 99percent accuracy on speed limit signs classification. The performance of SVM model highly depends on the choice of model parameters. Two search algorithms, grid search and simulated annealing search have been implemented to improve the performances of our classification model. The SVM model were also shown to be more effective than Fuzzy ARTMAP model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Parker:2008:ijcnn,
author = "Matt Parker and Bobby D. Bryant",
title = "Neuro-visual Control in the Quake II Game Engine",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1016.pdf},
url = {},
size = {},
abstract = {The first-person-shooter Quake II is used as a platform to test neuro-visual control and retina input layouts. Agents are trained to shoot a moving enemy as quickly as possible in a visually simple environment, using a neural network controller with evolved weights. Two retina layouts are tested, each with the same number of inputs: first, a graduated density retina which focuses near the center of the screen and blurs outward; second, a uniform retina which focuses evenly across the screen. Results show that the graduated density retina learns more successfully than the uniform retina. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Marquez:2008:ijcnn,
author = "Jose Manuel Marquez and Juan Antonio Ortega",
title = "Creating Adaptive Learning Paths using Ant Colony Optimization and Bayesian Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1017.pdf},
url = {},
size = {},
abstract = {This paper presents a new way to combine two different approaches of artificial intelligence looking for the best path in a graph, Ant Colony Optimization and Bayesian Networks. The main objective is to develop a learning management system which will have the capability of adapting the learning path to the learner's needs in execution time, taking into account the pedagogical weight of each learning unit and the system's social behavior. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang7:2008:ijcnn,
author = "Jian Huang and Xiaoming Chen and P C Yuen and Jun Zhang and W S Chen and J H Lai",
title = "Kernel Parameter Optimization for Kernel-based LDA Methods",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1018.pdf},
url = {},
size = {},
abstract = {Kernal approach has been employed to solve classification problem with complex distribution by mapping the input space to higher dimensional feature space. However, one of the crucial factors in the Kernel approach is the choosing of kernel parameters which highly affect the performance and stability of the kernel-based learning methods. In view of this limitation, this paper adopts the Eigenvalue Stability Bounded Margin Maximization (ESBMM) algorithm to automatically tune the multiple kernel parameters for Kernel-based LDA methods. To demonstrate its effectiveness, the ESBMM algorithm has been extended and applied on two existing kernel-based LDA methods. Experimental results show that after applying the ESBMM algorithm, the performance of these two methods are both improved. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Okada:2008:ijcnn,
author = "Shogo Okada and Osamu Hasegawa",
title = "On-line Learning of Sequence Data Based on Self-Organizing Incremental Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1020.pdf},
url = {},
size = {},
abstract = {This paper presents an on-line, continuously learning mechanism for sequence data. The proposed approach is based on SOINN-DTW method (Okada and Hasegawa, 2007), which is designed for learning of sequence data. It is based on Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping (DTW). Using SOINN's function represents the topological structure of online input data, the output distribution of each states is represented and adapted in a self-organizing manner corresponding to online input data. Consequently, this method can train a network and estimate parameters of the output distribution using new (on-line) data continuously, based on scarce batch-training data. Through online learning, the recognition accuracy is improved continuously. To confirm the effectiveness of the on-line learning mechanism of SOINN-DTW, we present an extensive set of experiments that demonstrate how our method outperforms the online learning method of HMM in classifying phoneme data. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Uchitani:2008:ijcnn,
author = "Yumiko Uchitani and Yoshifumi Nishio",
title = "Synchronization Patterns Generated in a Ring of Cross-Coupled Chaotic Circuits",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1021.pdf},
url = {},
size = {},
abstract = {Studies on chaos synchronization in coupled chaotic circuits are extensively carried out in various fields. In this study, synchronization patterns generated in a ring of crosscoupled chaotic circuits are investigated. Computer simulations show that this coupled system produces several phase patterns. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Altahhan:2008:ijcnn,
author = "Abdulrahman Altahhan and Kevin Burn",
title = "Visual Robot Homing Using Sarsa(λ), Whole Image Measure, and Radial Basis Function",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1022.pdf},
url = {},
size = {},
abstract = {This paper describes a model for visual homing. It uses Sarsa(λ) as its learning algorithm, combined with the Jeffery Divergence Measure (JDM) as a way of terminating the task and augmenting the reward signal. The visual features are taken to be the histograms difference of the current view and the stored views of the goal location, taken for all RGB channels. A radial basis function layer acts on those histograms to provide input for the linear function approximator. An on-policy on-line Sarsa(λ) method was used to train three linear neural networks one for each action to approximate the action-value function with the aid of eligibility traces. The resultant networks are trained to perform visual robot homing, where they achieved good results in finding a goal location. This work demonstrates that visual homing based on reinforcement learning and radial basis function has a high potential for learning local navigation tasks. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Acampora:2008:ijcnn,
author = "Giovanni Acampora and Matteo Gaeta",
title = "Optimizing Learning Path Selection Through Memetic Algorithms",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1023.pdf},
url = {},
size = {},
abstract = {e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Perfilieva:2008:ijcnn,
author = "Irina Perfilieva and Vilem Novak and Viktor Pavliska and Antonín Dvořak and Martin Štěpnička",
title = "Analysis and Prediction of Time Series Using Fuzzy Transform",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1024.pdf},
url = {},
size = {},
abstract = {A new methodology for forecasting of time series is proposed. It is based on combination of two techniques: fuzzy transform and perception-based logical deduction on the basis of learned linguistic description. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen14:2008:ijcnn,
author = "Cunjian Chen ",
title = "Information Fusion of Wavelet Projection Features for Face Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1025.pdf},
url = {},
size = {},
abstract = {This paper proposes a novel feature extraction method for face recognition in the wavelet domain called wavelet projection entropy (WPE). First, the projection entropy features from each wavelet subband are computed along the vertical and horizontal direction after the division. Then information fusion scheme is applied to integrate results obtained from each subband. Experiments show that WPE can extract the meaningful information from the wavelet domain. Meanwhile the decision level fusion achieves the best recognition rate among the three common information fusion methods. The proposed algorithms are validated on ORL and Yale face database for different pose and expression changes analysis. Detailed comparisons with previous published results are provided and it shows that our proposed algorithm performs very well. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mehboob:2008:ijcnn,
author = "Zareen Mehboob and Stefano Panzeri and Mathew E. Diamond and Hujun Yin",
title = "Topological Clustering of Synchronous Spike Trains",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1028.pdf},
url = {},
size = {},
abstract = {This paper describes a topological clustering of synchronous spike trains recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterized by different frequencies and amplitudes. Discrete spike trains are first interpreted as continuous synchronous activities by a smoothing filter such as causal exponential function. Then clustering is performed using the self-organizing map, which yields topologically ordered clusters of responses with respect to the stimuli. The grouping is formed mainly along the product of amplitude and frequency of the stimuli. This result coincides with the result obtained previously using mutual information analysis on the same data set. That is, the response is proportional in logarithm to the energy of the vibration. It suggests that such clustering can naturally find underlying stimulus-response patterns and it also seems to associate the spike-count based mutual information decoding with temporal patterns of the neuronal activities. The study also shows that causal decaying exponential kernel is better than noncausal Gaussian kernel in interpreting the discrete spike trains into continues ones and produces better clusters. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lo:2008:ijcnn,
author = "James Ting-Ho Lo ",
title = "Probabilistic Associative Memories",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1030.pdf},
url = {},
size = {},
abstract = {Recurrent multilayer network structures and Hebbian learning are two of the research results on thebrain that are widely accepted by neuroscientists. The for- mer led to multilayer perceptrons (MLPs) and recurrent MLPs, and the latter to associative memories. This pa- per presents recurrent and/or multilayer networks of novel associative memories, each being a new functional model of the neuron with its dendritic weights. The recurrent and/or multilayer networks are called probabilistic asso- ciative memory (PAMs) and the functional model of the neuron is called processing element. Each processing el- ement with its weights learns by the Hebbian rule and computes a subjective conditional probability as well as a point estimate of the class label of the cause(s) within its receptive ?eld. Detected and recognized causes are in- tegrated by the processing elements, aided by feedbacks, from layer to layer and from time to time into a spatial and/or temporal hierarchy of causes to facilitate under- standing of the pattern or sequence of patterns presented to the PAM. Mainly due to multilayer and recurrent struc- tures and Hebbian learning, PAMs have many such desir- able properties of a pattern recognizer or learning machine as (1) fast learning and responding to large temporal and spatial patterns; (2) detecting and recognizing multiple causes associatively and hierarchically; (3) having good generalization capabilities; (4) representing and resolving ambiguity and uncertainty with conditional probabilities },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li13:2008:ijcnn,
author = "Yanyan Li and Mingkai Dong and Ronghuai Huang",
title = "Special Interest Groups Discovery and Semantic Navigation Support within Online Discussion Forums",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1032.pdf},
url = {},
size = {},
abstract = {Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective e-learning facility lies in the continuously increasing postings but with discrete and incoherent structure as well as the loosely-tied learners with response-freeness. This paper proposes a hybrid approach to automatically discover special interest groups within discussion forums. Once a learner becomes a member of a special interest group, he will be informed of other learning companions to enhance their in-depth communication and learning, and the newly-emerged related information will be proactively pushed to him as well. Furthermore, by identifying the posting themes and types, this paper presents a semantic search to assist learners navigating through well-structured and coherent postings to meet their learning demands. The proposed approach has been integrated into a discussion forum, and the experimental results show that the approach is feasible and efficient, enabling the effective discovering of interest groups and proper demand-driven navigational guidance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wan:2008:ijcnn,
author = "Xin Wan and Toshie Ninomiya and Toshio Okamoto",
title = "A Learner's Role-based Multi Dimensional Collaborative Recommendation (LRMDCR) for Group Learning Support",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1033.pdf},
url = {},
size = {},
abstract = {This article argues for the new solution of personal recommender systems that can provide learners with suitable learning objects to learn in group learning. In order to improve the ``educational provision'' to implement the e-learning recommender system, we propose a new recommendation approach which has been proven to be more suitable to realize personalized recommendation based on not only learning histories but also learning activities and learning processes which is defined as LRMDCR (a Learner's Role-based Multi-dimensional Collaborative Recommendation) by us. In the approach, firstly we use the Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners' learning activities and learning processes. Secondly we use the multidimensional collaborative filtering to decide the recommendation learning objects to every learner of the group. We believe our approach is more effective and efficient to group learning. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Lin4:2008:ijcnn,
author = "Hsio-Yi Lin and An-Pin Chen",
title = "Application of Dynamic Financial Time-Series Prediction on the Interval Artificial Neural Network Approach with Value-at-Risk Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1037.pdf},
url = {},
size = {},
abstract = {Artificial Neural Networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a Fuzzy BPN, consisting of a Back-Propagation Neural Network (BPN) and a fuzzy membership function which takes advantage of the ANNs' nonlinear features and interval values instead of the shortcoming of ANNs' single-point estimation. To employ the two characteristics mentioned above, a dynamic intelligent time-series forecasting system will be built more efficiently for practical financial predictions. Additionally, with the liberalization and opening of financial markets, the relationships among financial commodities became much closer and complicated. Hence, establishing a perfect measure approach to evaluate investment risk has become a critical issue. The objective of this study is not only to achieve higher efficiency in dynamic financial time-series predictions but also a more effective financial risk control with Value-at-Risk methodology, which is called Fuzzy-VaR BPN model in this study. By extending to the financial market environment, it is expected that wider and more suitable applications in financial time-series and risk management problems would be covered. Moreover, the Fuzzy-VaR BPN model would be applied to the Taiwan Top50 Tracker Fund to demonstrate the capability of our study. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kouzani:2008:ijcnn,
author = "A. Z. Kouzani ",
title = "Subcellular Localisation of Proteins in Fluorescent Microscope Images Using a Random Forest",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1038.pdf},
url = {},
size = {},
abstract = {This paper presents a system that employs random forests to formulate a method for subcellular localisation of proteins. A random forest is aan ensemble learner that grows classification trees. Each tree produces a classification decision, and an integrated output is calculated. The system classifies the protein-localisation patterns witjin fluorescent microscope images. 2D images of HeLa cells that include all major classes of subcellular structures, and the associated feature set are used. The performance of the developed system is compared against that of the support vector machine and decision tree approaches. Three experiments are performed to study the influence of the training and test set size on the performance of the examined methods. The calculated classification errors and execution times are presented and discussed. The lowest classification error (2.9percent) has been produced by the developed system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(He4:2008:ijcnn,
author = "Fei He and Martin Brown and Lam Fat Yeung",
title = "On the Complexity — Sensitivity Trade-Off for the NF-κB Pathway Modeling",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1039.pdf},
url = {},
size = {},
abstract = {An important aspect of systems biology research is the so-called ''reverse engineering'' of cellular metabolic dynamics from measured input-output data. This allows researchers to estimate and validate both the pathway's structure as well as the kinetic constants. In this paper, a regularization based method which performs model structure selection is developed and applied to the problem of analyzing how existing pathway knowledge can be used as a prior investigate the model change complexity/sensitivity trade-off. Specifically, a 1-norm prior on parameter deviations from an existing model of the IκB-NF-κB pathway is combined with new experimental data and an analysis is performed to determine which are the most relevant components to alter. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Daqi:2008:ijcnn,
author = "Gao Daqi and Yang Zeping and Sun Jianli",
title = "Modular Neural Networks for Estimating Odor Concentrations",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1042.pdf},
url = {},
size = {},
abstract = {The concentration estimation for multiple kinds of odors is regarded first as multiple two-class classification and then as multiple approximation problems, and solved by multiple single-output multi-layer perceptrons (MLPs) lined up in two parallel rows. A pair of MLPs in cascade is on behalf of a specified odor. n pairs of MLPs represent n kinds of odors, one for one. An MLP in the first row separates its represented odor from the others. Because the two-class training subsets are often unbalanced, the samples from the minority sides are virtually reinforced. The generalization of an MLP is limited in local regions with respect to the distribution of the represented odor. An MLP in the second row approximates the relationship between the responses of the sensor array and the concentrations of the represented odor. A sample is assigned to a kind of odor by the MLP with the maximum output in the first row, and then its concentration is estimated by another MLP in the corresponding pair. The effectiveness of the proposed MLP models is verified by the experiments for 4 kinds of fragrant materials as well as their extended dataset. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Zhang13:2008:ijcnn,
author = "Wenle Zhang and Rutao Luo",
title = "An Adaptive Feedback Neural Network Approach to Job-shop Scheduling Problem",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1045.pdf},
url = {},
size = {},
abstract = {Job-shop scheduling problem is a typical representative of NP-complete problems and it is also a popular topic for the researchers during the recent decades. Lots of artificial intelligence techniques were used to solve this kind of problems, such as: Genetic Algorithm, Tabu Searching Method, Simulated Annealing and Neural Network. Based on the previous research of Zhou [2] and Willems [9], this paper proposes a neuro-dynamic model with two heuristics to solve job-shop scheduling problems. The stability of this neural network is proven by using Lyapunov Stability Theorem. Both small-size and big-size problems are used to test this neural network. Simulation results of some tested samples are given. And the performance of this neural network is compared with several other neural works under experimental conditions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wu3:2008:ijcnn,
author = "Yunfeng Wu and Yachao Zhou and Sin-Chun Ng and Yixin Zhong",
title = "Combining Neural-Based Regression Predictors Using an Unbiased and Normalized Linear Ensemble Model",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1046.pdf},
url = {},
size = {},
abstract = {In this paper, we combined a group of local regression predictors using a novel unbiased and normalized linear ensemble model (UNLEM) for the design of multiple predictor systems. In the UNLEM, the optimization of the ensemble weights is formulated equivalently to a constrained quadratic programming problem, which can be solved with the Lagrange multiplier. In our simulation experiments of data regression, the proposed multiple predictor system is composed of three different types of local regression predictors, and the effectiveness evaluation of the UNLEM was carried out on eight synthetic and four benchmark data sets. Results of the UNLEM's performance in terms of mean-squared error are significantly lower, in comparison with the popular simple average ensemble method. Moreover, the UNLEM is able to provide the regression predictions with a relatively higher normalized correlation coefficient than the results obtained with the simple average approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pasila:2008:ijcnn,
author = "Felix Pasila and Sautma Ronni and Thiang and Lie Handra Wijaya",
title = "Long-term Forecasting in Financial Stock Market using Accelerated LMA on Neuro-Fuzzy Structure and Additional Fuzzy C-Means Clustering for Optimizing the GMFs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1047.pdf},
url = {},
size = {},
abstract = {The paper describes the combination of two modeling strategies between the accelerated Levenberg- Marquardt algorithm (accelerated LMA) on neuro-fuzzy approach and fuzzy clustering algorithm C-Means that can be used to forecast financial stock market such as Jakarta Stock Indices (JCI) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The accelerated LMA algorithm is efficient in the common sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The C-Means fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low Sum Squared Error (SSE) value with given training data of neuro-fuzzy network, are further fine tuned during the network training. As a final point, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of Stock Market in Indonesia. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu15:2008:ijcnn,
author = "Nan Liu and Han Wang",
title = "Feature Selection in Frequency Domain and Its Application to Face Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1050.pdf},
url = {},
size = {},
abstract = {Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms (GAs) as well as dimensionality reduction methods are used to create a combined framework of feature acquisition. In details, the face images are first transformed to frequency domain through DCT. Then GAs are used to seek for optimal features in the redundant DCT coefficients where the generalization performance guides the searching process. The last step is to reduce the dimension of selected features. In experiments, two face databases are used to evaluate the effectiveness of the proposed method. In addition, an entropy-based improvement is also proposed. The experimental results present the superiority of selected frequency features. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chu:2008:ijcnn,
author = "Xiao-Lei Chu and Chao Ma and Jing Li and Bao-Liang Lu and Masao Utiyama and Hitoshi Isahara",
title = "Large-Scale Patent Classification with Min-Max Modular Support Vector Machines",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1054.pdf},
url = {},
size = {},
abstract = {Patent classification is a large-scale, hierarchical, imbalanced, multi-label problem. The number of samples in a real-world patent classification typically exceeds one million, and this number increases every year. An effective patent classifier must be able to deal with this situation. This paper discusses the use of min-max modular support vector machine (M3-SVM) to deal with large-scale patent classification problems. The method includes three steps: decomposing a large-scale and imbalanced patent classification problem into a group of relatively smaller and more balanced two-class subproblems which are independent of each other, learning these subproblems using support vector machines (SVMs) in parallel, and combining all of the trained SVMs according to the minimization and the maximization rules. M3-SVM has two attractive features which are urgently needed to deal with largescale patent classification problems. First, it can be realized in a massively parallel form. Second, it can be built up incrementally. Results from experiments using the NTCIR-5 patent data set, which contains more than two million patents, have confirmed these two attractive features, and demonstrate that M3-SVM outperforms conventional SVMs in terms of both training time and generalization performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang11:2008:ijcnn,
author = "Hao-Yung Yang and Jiin-Chyr Hsu and Yung-Fu Chen and Xiaoyi Jiang and Tainsong Chen",
title = "Using Support Vector Machine to Construct a Predictive Model for Clinical Decision-Making of Ventilation Weaning",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1055.pdf},
url = {},
size = {},
abstract = {Ventilator weaning is the process of discontinuing mechanical ventilation from patients with respiratory failure. Ventilator support should be withdrawn as soon as possible when it is no longer necessary in order to reduce the likelihood of known nosocomial complications and costs. Previous investigation indicated that clinicians were often wrong when predicting weaning outcome. The motivation of this study is that although successful ventilator weaning of ICU patients has been widely studied, indicators for accurate prediction are still under investigation. The goal of this study is to find a prediction model for successful ventilator weaning using variables such physiological variables, clinical syndromes, demographic variables, and other useful information. The data obtained from 231 patients who had been supported by mechanical ventilator for longer than 21 days within the period from Nov. 2002 to Dec. 2005 were studied retrospectively. Among them, 188 patients were recruited from the period within Nov. 2002 to Dec. 2004 and the other 43 patients from Jan. 2004 to Dec. 2005. All the patients were clinically stable before being considered to undergo a weaning trial. Twenty-seven variables in total were collected with only 6 variables reaching significant level (p < 0.05) were used for support vector machine (SVM) classification after statistical analysis. The results show that the constructed model is valuable in assisting clinical doctors to decide if a patient is ready to wean from the ventilator with the sensitivity, specificity, and accuracy as high as 94.74percent, 95.83percent, and 95.35percent, respectively. Further prospective bed side test is needed to verify the efficacy of the model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Luciw:2008:ijcnn,
author = "Matthew D. Luciw and Juyang Weng",
title = "Topographic Class Grouping with Applications to 3D Object Recognition",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1056.pdf},
url = {},
size = {},
abstract = {The cerebral cortex uses a large number of topdown connections, but the roles of the top-down connections remain unclear. Through end-to-end (sensor-to-motor) multilayered networks that use three types of connections (bottom-up, lateral, and top-down), the new Topographic Class Grouping (TCG) mechanism shown in this paper explains how the topdown connections influence (1) the type of feature detectors (neurons) developed and (2) their placement in the neuronal plane. The top-down connections boost the variations in the neuronal between class directions during the training phase. The first outcome of this top-down boosted input space is the facilitation of the emergence of feature detectors that are purer, measured statistically by the average entropy of the neurons' development. The relatively purer neurons are more ''abstract,'' i.e., characterizing class-specific (or motorspecific) input information, resulting in better classification rates. The second outcome of this top-down boosted input space is the increase of the distance between input samples that belong to different classes, resulting in a farther separation of neurons according to their class. Therefore, neurons that respond to the same class become relatively nearer. This results in TCG, measured statistically by a smaller within-class scatter of responses when the neuronal plane has a fixed size. Although these mechanisms are potentially applicable to any pattern recognition applications, we report quantitative effects of these mechanisms for 3D object recognition of center-normalized, background-controlled objects. TCG has enabled a significant reduction of the recognition errors. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Huang8:2008:ijcnn,
author = "Sue-Fn Huang and Liang-Ying Wei and Jr-Shian Chen and Ching-Hsue Cheng",
title = "RBF-NN Based Fusion Model for E-learning Achievement Evaluation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1057.pdf},
url = {},
size = {},
abstract = {The trend of using e-learning as a learning and teaching tool is widely adopted by numerous organizations. In order to enhance the e-learning efficiency, there are some advantages in e-learning system: (1) repeatable (learning), (2) timeless, (3) distanceless and (4) spaceless. Because ``student-centered'' instruction is likely to become the primary trend in education, the e-learning system should consider both of personalization and adaptability. By using the online examination, we can obtain the learning levels of students to adjust the learning schedule instantly for each one and build more adaptive e-learning system. But, the biases of assessments are assigned by teacher under un-controllable condition (i.e. tiredness, preference). To overcome the drawback, this paper proposes a fusion model to assign learning achievements based on RBF-NN (radial basis function-neural networks) for assisting teachers. Proposed model uses similarity threshold to remove inconsistent data and make our achievements evaluation more reliable. To verify our model, this paper collects e-learning online examination data to illustrate and compare the performance of proposed model with conventional RBF-NN model. The performance comparison results show that the proposed model outperforms the conventional RBF model. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mańndziuk:2008:ijcnn,
author = "Jacek Mańndziuk ",
title = "Some Thoughts on Using Computational Intelligence Methods in Classical Mind Board Games",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1061.pdf},
url = {},
size = {},
abstract = {In the last two decades the advancement of AI/CI methods in classical board and card games (such as Chess, Checkers, Othello, Go, Poker, Bridge, ...) has been enormous. In nearly all ''world famous'' board games humans have been decisively conquered by machines (actually Go remains almost the last redoubt of human supremacy). In the above perspective the natural question is whether there is still any need for further development of CI methods in this area. What kind of goals can be achieved on this path? What are (if any) the challenging problems in the field? The paper tries to discuss these issues with respect to classical board mind games and provides (highly subjective) partial answers to some of the open questions. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen15:2008:ijcnn,
author = "Yeou-Jiunn Chen and Jiunn-Liang Wu and Hui-Mei Yang",
title = "Automatic Speech Recognition and Dependency Network to Identification of Articulation Error Patterns",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1065.pdf},
url = {},
size = {},
abstract = {Articulation errors will seriously reduce speech intelligibility and the ease of spoken communication. Typically, a language therapist uses his or her clinical experience to identify articulation error patterns, a time-consuming and expensive process. This paper presents a novel automatic approach to identifying articulation error patterns and providing error information of pronunciation to assist the linguistic therapist. A photo naming task is used to capture examples of an individual's articulation patterns. The collected speech is automatically segmented and labeled by a speech recognizer. The recognizer's pronunciation confusion network is adapted to improve the accuracy of the speech recognizer. The modified dependency network and a multiattribute decision model are applied to identify articulation error patterns. Experimental results reveal the usefulness of the proposed method and system. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yang12:2008:ijcnn,
author = "Haiqin Yang and Kaizhu Huang and Irwin King and Michael R. Lyu",
title = "Efficient Minimax Clustering Probability Machine by Generalized Probability Product Kernel",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1068.pdf},
url = {},
size = {},
abstract = {Minimax Probability Machine (MPM), learning a decision function by minimizing the maximum probability of misclassification, has demonstrated very promising performance in classification and regression. However, MPM is often challenged for its slow training and test procedures. Aiming to solve this problem, we propose an efficient model named Minimax Clustering Probability Machine (MCPM). Following many traditional methods, we represent training data points by several clusters. Different from these methods, a Generalized Probability Product Kernel is appropriately defined to grasp the inner distributional information over the clusters. Incorporating clustering information via a non-linear kernel, MCPM can fast train and test in classification problem with promising performance. Another appealing property of the proposed approach is that MCPM can still derive an explicit worst-case accuracy bound for the decision boundary. Experimental results on synthetic and real data validate the effectiveness of MCPM for classification while attaining high accuracy. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Del-Moral-Hernandez:2008:ijcnn,
author = "Emilio Del-Moral-Hernandez ",
title = "RPE-BAM Networks: Bidirectional Heteroassociation in Neural Networks of Recursive Nodes with Rich Dynamics",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1072.pdf},
url = {},
size = {},
abstract = {This paper addresses networks of recursive processing elements (RPEs) that exhibit, even at the single node level, rich dynamics and switching between ordered, erratic (chaotic) and diverse periodic trajectories. These networks are considered here for the implementation of bidirectional heteroassociation. These newly proposed architectures are named RPE-BAM. Dynamic mixture of erratic and ordered dynamics is explored, during the episodes of: a) input prompting; b) search for the embedded heteroassociations compatible with the input pattern; c) the production of an heteroassociation pair. Concepts and design methods on RPEBAMs and on parametric coupling of recursive nodes are discussed, and numerical experiments are analyzed, showing robust operation of the RPE-BAM architecture. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hussin:2008:ijcnn,
author = "Mahmoud F. Hussin and Mahmoud R. farra and Yasser El-Sonbaty ",
title = "Extending the Growing Hierarchal SOM for Clustering Documents in Graphs Domain",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1073.pdf},
url = {},
size = {},
abstract = {The Growing Hierarchal Self-Organizing Map (GHSOM) is the most efficient model among the variants of SOM. It is used successfully in document clustering and in various pattern recognition applications effectively. The main constraint that limits the implementation of this model and all the other variants of SOM models is that they work only with Vector Space Model (VSM). In this paper, we extend the GHSOM to work in the graph domain to enhance the quality of clusters. Specifically, we represent the documents by graphs and then cluster those documents by using a new algorithm GGHSOM: Graph-based Growing Hierarchal SOM after modifying its operations to work with the graph instead of vector space. We have tested the G-GHSOM on two different document collections using three different measures for evaluating clustering quality. The experimental results of the proposed G-GHSOM show an improvement in terms of clustering quality compared to classical GHSOM. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pacheco:2008:ijcnn,
author = "Diogo F. Pacheco and Flavio R. S. Oliveira and Fernando B. Lima Neto",
title = "Including Multi-Objective Abilities in the Hybrid Intelligent Suite for Decision Support",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1074.pdf},
url = {},
size = {},
abstract = {Hybrid intelligent systems (HIS) are very successful in tackling problems comprising of more than one distinct computational subtask. For instance, decision-making problems are good candidates for HIS because of their frequent dual nature. This is because supporting decision-making most often involves two phases: (i) forecasting decision scenarios and (ii) searching in those scenarios. In addition to reducing the inherent uncertainty and effort in decision making, previous works in the area of decision support have shown that some of the inconveniences of the `Inverse Problem' can be overcome by the use of Hybrid Intelligent Decision Suites (HIDS). This paper extends HIDS by including a third module that deals with multi-objective (MO) tasks through Evolutionary Multi- Objective Optimization (EMOO). This EMOO module helps by creating the Pareto front for each forecast scenario produced by Artificial Neural Networks (ANN), acting here as the predictive engine of the decision support system. In order to interface better with decision makers, we use a fuzzy-heuristic module of the original HIDS. To test this concept we have applied our new approach to two distinct problems: (1) diagnosis of heart diseases (of the proben-1 data-set) and (2) automobile feature selection (of UCI data-set). Results have indicated that this new ensemble of intelligent techniques enhances the quality of decision making. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Koyama:2008:ijcnn,
author = "Jumpei Koyama and Masahiro Kato and Akira Hirose",
title = "Distinction Between Handwritten and Machine-Printed Characters with no Need to Locate Character or Text Line Position",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1075.pdf},
url = {},
size = {},
abstract = {In this paper, we propose a method for distinction between handwritten and machine-printed characters with no need to locate positions of characters or text lines. We call the proposed method `spectrum-based local fluctuation detection method. The method transforms local regions in document images into power spectrum to extract feature values which represent fluctuations caused by handwriting. We employ a multilayer perceptron for the distinction. We feed the obtained feature values to a preliminarily optimized multilayer perceptron (MLP), and the MLP yields likelihood of handwriting. We prepare a document image which has randomly aligned characters for an experiment. The experimental result shows that our method can distinguish handwritten and machine-printed characters with no need to locate positions of characters or text lines. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Liu16:2008:ijcnn,
author = "Xiaoming Liu and Zhaohui Wang and Jun Liu and Zhilin Feng",
title = "Face Recognition with Locality Sensitive Discriminant Analysis Based on Matrix Representation",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1076.pdf},
url = {},
size = {},
abstract = {Locality Sensitive Discriminant Analysis (LSDA) algorithm is a new data analysis tool for studying the class relationship between data points, which can use local geometry structure of the data manifold and discriminant information at the same time. A major disadvantage of LSDA is it that can only deal with vector data, and thus is often confronted with singularity problem. In this paper, an extension of LSDA is proposed, called two-dimensional locality sensitive discriminant analysis (2DLSDA), which is directly based on 2D image matrices for face recognition, can overcome the singularity problem and use the spatial information among pixels more effectively. Besides, based on the Schur decomposition, the projection matrices can be obtained efficiently with high numerical stablity, and orthogonality of projection matrix is guaranteed. Experiments on both ORL and Yale datasets demonstrate that the proposed method can achieve better performance than PCA, LDA and LSDA methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Feng2:2008:ijcnn,
author = "Du Feng and Qian Qingquan ",
title = "Heterogeneous Wireless Networked Control Systems Based on Modify Smith Predictor and CMAC-PID Control",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1078.pdf},
url = {},
size = {},
abstract = {The cerebellar model articulation controller (CMAC) neural network is a practical tool for improving existing nonlinear control systems, and it can effectively reduce tracking error of control system. In order to effectively restrain the impact of network delays for wireless networked control systems (WNCS), a novel approach is proposed that modified Smith predictor combined with CMAC-PID control for the heterogeneous wireless networked control systems (HWNCS). The HWNCS adopts cascade control system structure, use P control and CMAC-PID control, and data communications adopt heterogeneous wireless networks in the inner and outer loops. Based on modified Smith predictor, achieve complete compensations for the delays of networks and controlled plants. Because modified Smith predictor does not include network delay models, it is no need for measuring, identifying or estimating network delays on line. Therefore it is applicable to some occasions that network delays are larger than one, even tens of sampling periods. Based on IEEE 802.15.4 (ZigBee) in the inner loop and IEEE 802.11b/g (WLAN) in the outer loop, and there are data packets loss in the loops. The results of simulation show validity of the control scheme, and can improve dynamic performance, enhance robustness, self-adaptability and anti-jamming ability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ergüt:2008:ijcnn,
author = "Salih Ergüt and Ramesh R. Rao and Özgür Dural",
title = "Localization via Multipath Strengths in a CDMA2000 Cellular Network Using Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1079.pdf},
url = {},
size = {},
abstract = {Localization is becoming more important with increasing number of cellular phone users. Due to safety aspects with increased emergency calls from mobile phones, new applications related to location based services, and the network optimization with increasing load, localization draws interest from both the academia and the industry. In this study, we propose a neural network based algorithm that uses multipath strengths to locate a mobile user without a GPS receiver. We validated our algorithm in a commercial network. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Pham:2008:ijcnn,
author = "Minh Tuan Pham and Kanta Tachibana and Eckhard Hitzer and Sven Buchholz and Takeshi Furuhashi",
title = "Feature Extractions with Geometric Algebra for Classification of Objects",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1080.pdf},
url = {},
size = {},
abstract = {Most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of hand-written digits, which were classified by feature extraction with the proposed method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Vasudevan:2008:ijcnn,
author = "Bintu G. Vasudevan and Sorawish Dhanapanichkul and Rajesh Balakrishnan",
title = "Flowchart Knowledge Extraction on Image Processing",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1081.pdf},
url = {},
size = {},
abstract = {In the paper, we present an approach of image processing analysis to extract flowchart information from digital imagery. Firstly, flowchart imagery is processed to extract the text components and then extract the geometrical shapes components. We analyze text, and various geometrical shapes present in flowchart and carry out a variety of processes such as image segmentation, shape description, text and geometric components extraction, recognition and linking. The text components are extracted and then geometrical components are extracted. we also proposed a auto directional transformation of contour chain method for shape description. The internal relationship between the components is set up by tracing the flow lines which connect different components. Thus a flowchart is correctly extracted. The extracted components are output to metadata (XML format) which is machine readable. These metadata can be archived, store as knowledge base or shared with others. Finally, an example is presented and the results show that the proposed technique is efficient. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ma2:2008:ijcnn,
author = "Liying Ma ",
title = "Facial Expression Recognition Using 2-D DCT of Binarized Edge Images and Constructive Feedforward Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1082.pdf},
url = {},
size = {},
abstract = {Computer-based automatic human facial expression recognition (FER) is fundamental and indispensable in realizing truly intelligent human-machine interfaces. In this paper, a new FER technique is proposed, which uses lowerfrequency 2-D DCT coefficients of binarized edge images and constructive one-hidden-layer (OHL) feedforward neural networks (NNs). The 2-D DCT is thereby used to compress the binarized edge images to capture the important features for recognition. Constructive OHL NNs are then used to realize the mapping from the feature space to facial expression space. Facial expression ''neutral'' is regarded as a subject of recognition in addition to two other expressions, ''smile'' and ''surprise''. The proposed recognition technique is applied to two databases which contain 2-D front face images of 60 men (database (a)) and 60 women (database (b)), respectively. Experimental results reveal that our proposed technique provides in general improved performance when compared to two other recognition methods that use vector matching and fixed-size BP-based NNs. Our method yields testing recognition rates as high as 100percent and 95percent for databases (a) and (b), respectively, which clearly demonstrates its promising capabilities. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hamidian:2008:ijcnn,
author = "Hajar Hamidian and Hamid Soltanian-Zadeh and Reza Faraji-Dana and Masoumeh Gity",
title = "Comparison of Two Linear Models for Estimating Brain Deformation during Surgery Using Finite Element Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1085.pdf},
url = {},
size = {},
abstract = {This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume finite deformation of the brain after opening the skull. We use a test sphere as a model of the brain, tetrahedral finite element mesh, and function optimization that optimizes the models' parameters by minimizing the distance between the resulting deformation and the supposed deformation. Based on the final value of the objective function, we conclude that the accuracy of the solid mechanic model is higher than that of the elastic model. Applications of the methods to the MR images of the brain confirm this finding. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang12:2008:ijcnn,
author = "X. Wang and S. N. Balakrishnan",
title = "Optimal Controller Synthesis of Variable-Time Impulsive Problems Using Single Network Adaptive Critics",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1086.pdf},
url = {},
size = {},
abstract = {This paper presents a systematic approach to solve for the optimal control of a variable-time impulsive system. First, optimality condition for a variable-time impulsive system is derived using the calculus of variations method. Next, a single network adaptive critic technique is proposed to numerically solve for the optimal control and the detailed algorithm is presented. Finally, two examples-one linear and one nonlinear-are solved applying the conditions derived and the algorithm proposed. Numerical results demonstrate the power of the neural network based adaptive critic method in solving this class of problems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Meng:2008:ijcnn,
author = "Fei Meng and Kai-yu Tong and Suk-tak Chan",
title = "BCI-FES Training System Design and Implementation for Rehabilitation of Stroke Patients",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1092.pdf},
url = {},
size = {},
abstract = {A BCI-FES training platform has been designed for rehabilitation on chronic stroke patients to train their upper limb motor functions. The conventional functional electrical stimulation (FES) was driven by users' intention through EEG signals to move their wrist and hand. Such active participation was expected to be important for motor rehabilitation according to motor relearning theory. The common spatial pattern (CSP) algorithm was applied as one pre-processing step in brain-computer interface (BCI) module to search for the optimal spatial projection direction after brain reorganization. The pre- and post- clinical assessment was conducted to identify the possible functional improvement after the training. Two chronic stroke subjects attended this pilot study and the error rate of the BCI control was less than 20percent after training of 10 sessions. This implementation showed the feasibility for stroke patients to accomplish the BCI triggered FES rehabilitation training. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Park3:2008:ijcnn,
author = "Hogun Park and Yoonjung Choi and Yuchul Jung and Sung-Hyon Myaeng",
title = "Supporting Mixed Initiative Human-Robot Interaction: A Script-Based Cognitive Architecture Approach",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1093.pdf},
url = {},
size = {},
abstract = {As complex indoor-robot systems are developed and deployed into the real-world, the demand for human-robot interaction is increasing. Mixed-initiative human-robot interaction is a good method to coordinate actions of a human and a robot in a complementary fashion. In order to support such interactions, we employ scripts that are rich, flexible, and extensible for a robot's interactions in a variety of situations. Scripts are amenable for expressing knowledge in an applicable form, especially describing a sequence of actions in organizing tasks. In this paper, we propose a script-based cognitive architecture for collaboration, which is based on three-level cognitive models. It incorporates Dynamic Bayesian Network (DBN) to automatically govern action sequences in the scripts and detect user's intention or goal. Starting from an understanding of user initiatives, our intelligent task manager suggests the most relevant initiatives for an efficient collaboration. DBN has been evaluated in real indoor task scenarios for its efficacy in interaction reduction, error minimization, and task satisfaction. },
keywords = { Mixed-Initiative Interaction, Dynamic Bayesian Network, Human-Robot Interaction, Script, Robot-Task Script},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Jung2:2008:ijcnn,
author = "Jae-Yoon Jung and Janice I. Glasgow and Stephen H. Scott",
title = "Trial Map: A Visualization Approach for Verification of Stroke Impairment Assessment Database",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1095.pdf},
url = {},
size = {},
abstract = {Robotic/mechanic devices have become widely used for various medical assessments recently. While using these devices are beneficial in terms of accuracy and objectiveness, validation and consistency problem may occur when combining these data with traditional clinical information. Here we propose a visualization tool that can summarize the experimental data and compare them with the clinical data, in the stroke impairment assessment domain. This visual tool is based on a neural network ensemble that is trained to match the experimental data with Chedoke-McMaster scale, one of the major outcome measure for stroke impairment and recovery assessment. We compare our ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our visualization approach is consistent with clinical information, and reliable in a sense that output of our ensemble can be an estimator for the corresponding clinical data when Chedoke-McMaster scores are missing. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Smith-Miles:2008:ijcnn,
author = "Kate A. Smith-Miles ",
title = "Towards Insightful Algorithm Selection For Optimisation Using Meta-Learning Concepts",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1096.pdf},
url = {},
size = {},
abstract = {In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several metaheuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Inoue:2008:ijcnn,
author = "Takashi Inoue and Masaru Nakano and Yoshifumi Nishio",
title = "Output Characteristics of Cellular Neural Networks Using Mixture Template",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1101.pdf},
url = {},
size = {},
abstract = {In this research, we propose cellular neural networks using mixture template as an example of space-varying cellular neural networks. As the first step of the investigation of such complex nonlinear circuit networks, we propose two mixing methods of the templates and investigate the output characteristics of the simple image processing with a binary image and a grayscale image by computer simulations. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Choi2:2008:ijcnn,
author = "Hyun-Chul Choi and Sam-Yong Kim and Sang-Hoon Oh and Se-Young Oh and Sun-Young Cho",
title = "Pose Invariant Face Recognition with 3D Morphable Model and Neural Network",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1102.pdf},
url = {},
size = {},
abstract = {This paper introduces a pose invariant face recognition method with a training image and a query image using 3D morphable model and neural network. Our system uses 3D morphable model to get the reconstructed 3D face from the training image and obtains 2D image patches of facial components from the 3D face under varying head pose. The 2D image patches are used to train a neural network for pose invariant face recognition. Because those patches are obtained from the varying head pose, the neural network has robustness in the query image under the different head pose form the training image. Our pose invariant face recognition system has the performance of correct recognition higher than 98percent with BJUT 3D scan database. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Adhyaru:2008:ijcnn,
author = "Dipak M. Adhyaru and I. N. Kar and M. Gopal",
title = "Constrained Optimal Control of Bilinear Systems using Neural Network Based HJB Solution",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1105.pdf},
url = {},
size = {},
abstract = {In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm is proposed for a bilinear system. Using the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the system states. In the proposed algorithm, Neural Network (NN) is used to find approximate solution of HJB equation using least squares method. Proposed algorithm has been applied on bilinear systems. Necessary theoretical and simulation results are presented to validate proposed algorithm. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kuroe:2008:ijcnn,
author = "Yasuaki Kuroe and Yuriko Taniguchi",
title = "Models of Complex-Valued Dynamic Associative Memories and Analysis of Their Dynamics -Analytic and Non-analytic Activation Functions-",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1107.pdf},
url = {},
size = {},
abstract = {Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. Associative memories should recall memory patterns, and their dynamics are greatly affected by activation functions and connection weights. The theoretical analysis on qualitative properties of neural networks is very important to associative memories. We already proposed some models of complex valued associative memory using nonlinear bounded complex functions, which are not analytic. In this paper, we present several models of orthogonal type and auto-correlation type associative memories using several nonlinear complex functions which include analytic and nonanalytic functions, and investigate their behavior as associative memories theoretically. Comparisons are made among these models in terms of dynamics. Simulation studies are also done to investigate dynamics of an associative memory with singular points. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kuznetsov:2008:ijcnn,
author = "V. A. Kuznetsov and E. Motakis and A. V. Ivshina",
title = "Low- and High-Agressive Genetic Breast Cancer Subtypes and Significant Survival Gene Signatures",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1113.pdf},
url = {},
size = {},
abstract = {We characterize three small gene signatures derived consequently from the original 232-gene breast cancer aggressiveness signature which could improve biological classification and clinical assignment of ~ 50percent of breast cancer patients having histologic grade 2 tumors [3]. Here, we develop a novel approach to identify small gene signatures providing statistically reliable, biological important and clinical significant molecular markers. We consider three small molecular signatures which strongly represent three specific groups of genes related to (i) cell cycle/mitosis, (2) chromosome segregation and microtubular formation, (3) cell-cell communication, extracellular/immune signaling, and RNA binding. These results shed light on underlined biological mechanisms of lowaggressive and high-aggressive human breast cancer phenotypes and support our suggestion that re-classification of grade 2 breast tumors onto tumor grade 1-like and tumor grade 3-like subtypes can be related to two genetically and clinically distinct cancer types. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Mohan:2008:ijcnn,
author = "Permanand Mohan ",
title = "A Teacher for Every Learner: Rising to the Challenge with Computational Intelligence",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1114.pdf},
url = {},
size = {},
abstract = {A few years ago, providing a teacher for every learner was proposed as one of five Grand Research Challenges in Computer Science and Engineering. Although current research interest with learning objects is on the decline, this paper argues that they can still play a major role in meeting the Grand Challenge. In particular, the paper discusses the granularity, sequencing, and context aspects of learning objects, showing how these aspects are at the heart of personalization in an e-learning system. However, catering for granularity, sequencing, and context in an instructionally principled fashion are difficult computational problems. The paper discusses and proposes a range of computational intelligence techniques that can address these problems and thus contribute to achieving the vision of a teacher for every learner. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Garcia:2008:ijcnn,
author = "Francis Garcia and Ernesto Araujo",
title = "Visual Multi-Target Tracking by using Modified Kohonen Neural Networks",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1115.pdf},
url = {},
size = {},
abstract = {A visual target tracking identification by employing using a ``Winner-takes-all'' artificial neural network is proposed in this paper. In this approach a modified Kohonen Neural Network is the mechanism used both to determine the position as to represent the target trajectory given a sequence of images. Some of the advantages employing this technique is that the initial condition are supplied randomly and that the performance of the algorithm is independent of the initial condition as well as of the number of them. Besides, this algorithm converge for the center of mass of the target. This methodology is useful in remote and local systems when information is given by images be it related to aerospace applications, robotics, radar systems, or industrial applications. The proposed algorithm is here used in the identification of airplane trajectory by using digital images. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Júnior:2008:ijcnn,
author = "Francisco Chagas de Lima Júnior and Jorge Dantas de Melo and Adrião Duarte Doria Neto",
title = "",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1821-3",
file = {NN1116.pdf},
url = {},
size = {},
abstract = {Currently many non-tractable considered problemshave been solved satisfactorily through methods of approximateoptimization called metaheuristic. These methods usenon-deterministic approaches that find good solutions which,however, do not guarantee the determination of the global optimum.The success of a metaheuristic is conditioned by capacityto adequately alternate between exploration and exploitationof the solution space. A way to guide such algorithms whilesearching for better solutions is supplying them with moreknowledge of the solution space (environment of the problem).This can to be made in terms of a mapping of such environmentin states and actions using Reinforcement Learning. This paperproposes the use of a technique of Reinforcement Learning -Q-Learning Algorithm - for the constructive phase of GRASPand Reactive GRASP metaheuristic. The proposed methods willbe applied to the symmetrical traveling salesman problem. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Kwok:2008:fuzz,
author = "Antares San-Chin Kwok and Wai-Chuen Gan and Norbert C. Cheung",
title = "Improvements in the Motion Accuracy of Linear Switched
Reluctance Motors",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0002.pdf},
url = {},
size = {},
abstract = {During the last decade, the Linear Switched Reluctance Motor (LSRM) has become popular due to its structural simplicity, robustness and high power density. However, its significant torque ripple creates difficulty on precision motion control. This paper aims to develop a robust control system to improve the motion accuracy of LSRMs. The LSRM prototype is firstly investigated to study its force and current relationship. With the help of software, LSRM motion tests are simulated before real experiment. The significant improvement on position control strongly proves the success of the proposal. After that, the experimental result applying on the real prototype closely matches the simulation result. In order to enhance the LSRM robustness and the position tracking responses, another fuzzy logic controller is newly designed and implemented to supervise the traditional Proportional-Differential (PD) control parameters. Combining the inner control loop on current force relationship and the outer control loop on PD parameter supervision, the LSRM system in this project is very robust and capable to provide a high precision motion performance. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Du:2008:fuzz,
author = "Lixia Du and Xu Xu and Yan Cao and Jiying Li",
title = "A Novel Approach to Find the Satisfaction Pattern of Customers in Hotel Management",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0004.pdf},
url = {},
size = {},
abstract = {Nowadays, many studies of the discovery of needs and feelings of the hotel customers are not only around before-booking period, but also do not consider the privacy of customers completely. While the best period of studies of this knowledge are after the booking took place, there are two major problems for its unpopular: one is personal privacy, the other is not having a scientific and valuable approach. In this paper, we propose a novel approach to deal with the above existing problems. We employ intuitionistic fuzzy set, α-cuts, and Apriori algorithm to discovery the knowledge of needs and feelings of customers under an anonymous way. The approach is expatiated under different α by an example. And The yielded pattern and association rules have taken to the cooperative hotel more effects than before. So the approach is provable and valuable. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shirvanian:2008:fuzz,
author = "Marcel Shirvanian and Wolfram Lippe ",
title = "Optimization of the Normalization of Fuzzy Relational Databases by Using Alternative Methods of Calculation for the Fuzzy Functional Dependency",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0009.pdf},
url = {},
size = {},
abstract = {Although, in comparison to standard databases, a tremendous benefit is often derived by using fuzzy databases, their distribution is very low. A reason for the relatively poor acceptance of fuzzy relational databases is to be seen in the difficulty to carry out an adequate normalization. The various procedures discussed in the literature possess miscellaneous weaknesses. In this work a normalization method is regarded whose most significant deficit lies in the Fuzzy Functional Dependency (FFD) because thereby comprehensible results are not produced. Therefore, it is registered which alternatives for the determination of the degree of FFD exist. Furthermore, it is examined with which of these methods the just addressed disadvantage can be eliminated. For this purpose, the presented methods are applied to several examples in order to identify their characteristics. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chen:2008:fuzz,
author = "Gang Chen ",
title = "Discussion of Approximation Error Bounds to the Class of Fuzzy System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0011.pdf},
url = {},
size = {},
abstract = {The standard fuzzy systems are established with partition of normal quadratic polynomial membership functions and normal trigonometric membership functions. Based on the systems established and the standard fuzzy systems with partition of normal triangle functions, approximation error bounds problems are discussed by interpolation theory. Universal approximation error bounds of these fuzzy systems from SISO to MISO are given and their relations are founded. The error remainder term and auxiliary function are employed for the first time in proving process. Moreover, advantage and shortcoming of the three fuzzy systems are compared and correlative conclusions are obtained. Finally, computing examples are given and the validity of the conclusions is confirmed. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cheng:2008:fuzz,
author = "Ba-yi Cheng and Hua-ping Chen and Shuan-shi Wang ",
title = "Fuzzy Scheduling for Single Batch-processing Machine with Non-identical Job Sizes",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0016.pdf},
url = {},
size = {},
abstract = {In this paper, we introduce the fuzzy model of the makespan on a single batch-processing machine with non-identical job sizes and propose an improved DNA evolutionary algorithm (IDEA) solution approach. The model is based on fuzzy batch processing time and fuzzy intervals between batches. DEA is improved by integrating the crossover operator to overcome the immature convergence caused by the determinate selection of vertical operator in DEA. To decode the permutations of jobs searched by IDEA, the heuristic first fit decreasing (FFD) is applied to produce batches. In the experiment, the results of the fuzzy makespan demonstrate the proposed algorithm outperforms GA and SA on all instances. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yamada:2008:fuzz,
author = "Koichi Yamada and Osamu Onosawa and Muneyuki Unehara",
title = "Simulating Associations and Interactions Among Multiple Pieces of Brand Image Using Fuzzy Bidirectional Associative Memory",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0017.pdf},
url = {},
size = {},
abstract = {The paper discusses an idea of representing brand image on a computer and simulating associations and interactions among multiple pieces of brand image. Brand image is represented using a fuzzy set based on the theory of brand personality, which is a theory to represent brand image indirectly by a set of human characteristics associated with a brand. An convenient feature of the representation is generality that image of any kind of brands could be defined on the same universal set. The interactions among multiple pieces of image are simulated using the framework of Conceptual Fuzzy Set which is realized as combination of two Fuzzy Bidirectional Associative Memories. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chang:2008:fuzz,
author = "Tsung-Han Chang and Tien-Chin Wang",
title = "Fuzzy Preference Relation Based Multi-Criteria Decision Making Approach for WiMAX License Award",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0019.pdf},
url = {},
size = {},
abstract = {This paper develops a multi-criteria evaluation approach based on the preference relation to help the National Communication Commission (NCC) in Taiwan award a WiMAX license under fuzzy environment, where the vagueness and subjectivity are handled with linguistic variables parameterized by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision making (MCDM) method to determine the importance weights of evaluation criteria and synthesize the ratings of possible alternatives. Aggregated the evaluators' attitude toward possible alternatives; then the non-dominated degree is employed to obtain a crisp overall performance value for each contender to make a final decision. This approach is demonstrated with a real case study involving seven evaluation criteria, eight mobile companies assessed by four evaluators from academia and telecommunication arena. },
keywords = {Multiple criteria decision making, fuzzy sets theory, preference relation, },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu:2008:fuzz,
author = "Jianhua Xu and Yaning Chen and Weihong Li",
title = "Grey Modelling the Groundwater Level Dynamic in the Lower Reaches of Tarim River Affected by Water Delivery from Upper Reaches: A Demonstration from Yingsu Section",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0020.pdf},
url = {},
size = {},
abstract = {Using the grey system theory and the monitored data from the monitoring section of Yingsu, this paper models the groundwater level dynamic in the lower reaches of Tarim River affected by water delivery from upper reaches. The main conclusions are: (1) Discharging volume, running days for water delivery and daily discharging volume, which related with water delivery from the upper reaches of Tarim River, are three main factors that markedly control and affect the groundwater level. (2) The sensitivity of groundwater level changing respond to itself becomes more and more lower versus the distance apart from river center, and the affection from discharging volume and running days for water delivery to the change rate of groundwater level becomes more and more significant with increase of the distance apart from river center. Water delivery not only markedly controls and raises the groundwater level near river, but also affects the groundwater level as far as the range in the distance of 1050 m apart from river center. },
keywords = { groundwater level, dynamic, the lower reaches of Tarim River, water delivery, grey system, modelling},
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Shiwang:2008:fuzz,
author = "Hou Shiwang and Tong Shurong",
title = "Fuzzy Logic Based Assignable Causes Ranking System for Control Chart Abnormity Diagnosis",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0022.pdf},
url = {},
size = {},
abstract = {When using control chart patterns as signals to identify the cause for faster and easier process diagnosis, tradition method is hard to handle with the uncertainties, ambiguities and vagueness associated with the problem. Based on fuzzy logic, this paper develops a fuzzy inference system (FIS), composed by six sub modules. Each determines the intensity of corresponding causes based on degree of presence of each pattern. All the evidence supporting each cause from the unnatural patterns are aggregated using fuzzy connective operators and causes are prioritized according to the final aggregating results. The search can be done from the cause having highest priority when process goes out of control. },
keywords = {fuzzy inference system, process abnormity diagnosis, assignable causes ranking },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hao:2008:fuzz,
author = "Fei Hao and Zheng Pei and Shengtong Zhong",
title = "Searching Minimal Attribute Reduction Sets Based on Combination of the Binary Discernibility Matrix and Graph Theory",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0023.pdf},
url = {},
size = {},
abstract = {Attribute reduction plays an important role in rough set theory. It is an important application in data mining. In this paper, we focus on discussing the relation between set covering and attribute reduction in rough set theory. Based on the equivalence between minimal set covering and minimal attribute reduction sets, attribute reduction graph (ARG) is constructed. A novel algorithm to find the minimal attribute reduction sets, which is based on combination of binary discernibility matrix and graph theory is proposed in this paper. This algorithm demonstrates its efficiency and feasibility by an example. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Seki:2008:fuzz,
author = "Hirosato Seki and Hiroaki Ishii ",
title = "On the Monotonicity of Functional Type SIRMs Connected Fuzzy Reasoning Method and T-S Reasoning Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0024.pdf},
url = {},
size = {},
abstract = {Yubazaki et al. have proposed "single input rule modules connected type fuzzy reasoning method" (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the inference results from single input fuzzy rule module. Moreover, Seki et al. have proposed "functional type single input rule modules connected fuzzy reasoning method" (functional type SIRMs method, for short) whose consequent parts are generalized to functions from real numbers. It is expect that inference results of functional type SIRMs method have monotonicity if the antecedent parts and consequent parts of fuzzy rules in the functional type SIRMs rule modules have monotonicity. However, this paper points out that even if fuzzy rules in functional type SIRMs rule modules have monotonicity, the inference results do not necessarily have monotonicity. Moreover, it clarifies the conditions for the monotonicity of inference results by functional type SIRMs method, Takagi-Sugeno reasoning method (T-S reasoning method, for short), and simplified fuzzy reasoning method. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang:2008:fuzz,
author = "Ning Wang and Xianyao Meng",
title = "Analytical Structures and Stability Analysis of Three-Dimensional Fuzzy Controllers",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0026.pdf},
url = {},
size = {},
abstract = {We have revealed the analytical structures and stability analysis of the three-dimensional fuzzy controllers involving trapezoidal input fuzzy sets, singleton output fuzzy sets, Zadeh fuzzy AND triangular norm, Zadeh fuzzy OR triangular co-norm, Mamdani inference method and centroid defuzzification algorithm. This class of fuzzy controllers is a combination of a nonlinear PID controller with dynamic proportional gain, dynamic integral gain and dynamic derivative gain plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. A computer simulation is provided to illustrate that the new fuzzy controller is effective and superior to the conventional PID controller. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wang2:2008:fuzz,
author = "Ning Wang and Xianyao Meng",
title = "Analysis of Structure and Stability for The Simplest Two-Dimensional Fuzzy Controller Using Generalized Trapezoid-Shaped Input Fuzzy Sets",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0027.pdf},
url = {},
size = {},
abstract = {By summarizing the common characteristics of popular triangular and trapezoidal fuzzy sets, the more extensive generalized trapezoid-shaped (GTS) fuzzy set has been proposed. We have contributed to the analytical structures and stability analysis of the simplest two-dimensional fuzzy controllers using GTS input fuzzy sets. This class of fuzzy controllers is a combination of a piecewise linear PI controller plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. Two computer simulations are provided to demonstrate that the new fuzzy controller is effective and superior to the conventional PID controller. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chang2:2008:fuzz,
author = "Pei-Chann Chang and Chin-Yuan Fan and Chia-Hsuan Yeh and Wan-Ling Pan",
title = "A Hybrid System by Integrating Case Based Reasoning and Fuzzy Decision Tree for Financial Time Series Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0028.pdf},
url = {},
size = {},
abstract = {Stock price predictions suffer from two well known difficulties, i.e., complicated and non-stationary variations within the large historic data. This paper establishes a novel financial time series-forecasting model by a case based fuzzy decision tree induction for stock price movement predictions in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately react to the current tendency of the stock price movement from these smaller case based fuzzy decision tree inductions. Hit rate is applied as a performance measure and the effectiveness of our proposed CBFDT model is demonstrated by experimentally compared with other approaches on various stocks from TSEC. The average hit rate of CBFDT model is 91percent the highest among others. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Cao:2008:fuzz,
author = "Jiangtao Cao and Honghai Liu and Ping Li and David Brown",
title = "Adaptive Fuzzy Logic Controller for Vehicle Active Suspensions with Interval Type-2 Fuzzy Membership Functions",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0029.pdf},
url = {},
size = {},
abstract = {Elicited from the Least Means Squares optimal algorithm (LMS), an adaptive fuzzy logic controller (AFC) based on interval type-2 fuzzy sets is proposed for vehicle non-linear active suspension systems. The interval membership functions (IMF2s) are used in the AFC design to deal with not only non-linearity and uncertainty caused from irregular road inputs and immeasurable disturbance, but also the potential uncertainty of expert's knowledge and experience. The adaptive strategy is designed to self-tune the active force between the lower bounds and upper bounds of interval fuzzy outputs. A case study based on a quarter active suspension model has demonstrated that the proposed type-2 fuzzy controller significantly outperforms conventional fuzzy controllers of an active suspension and a passive suspension. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wai:2008:fuzz,
author = "Rong-Jong Wai and Zhi-Wei Yang",
title = "Adaptive Fuzzy-Neural-Network Control of Robot Manipulator Using T-S Fuzzy Model Design",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0031.pdf},
url = {},
size = {},
abstract = {This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNC system is investigated without the requirement of prior system information. In this model-free control scheme, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with on-line learning ability is constructed for representing the system dynamics of an n-link robot manipulator. Then, a four-layer fuzzy-neural-network (FNN) is used for estimating nonlinear dynamic functions in this fuzzy model. Moreover, the AFNNC law and adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the network convergence as well as stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed AFNNC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), Takagi-Sugeno-Kang (TSK) type fuzzy-neural-network control (T-FNNC), robust-neural-fuzzy-network control (RNFNC), and fuzzy-model-based control (FMBC) systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(M.:2008:fuzz,
author = "Mario I. Chacón M. and Juan I. Nevarez S. ",
title = "A Fuzzy Clustering Approach on the Classification of Non Uniform Cosmetic Defects",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0032.pdf},
url = {},
size = {},
abstract = {In this paper a fuzzy clustering approach for the classification of cosmetic defects is presented. The paper investigates the solution of this classification problem with the Gustafson-Kessel (GK), and Geth-Geva (GG) with Abonyi-Szeifert (AS) fuzzy algorithms. The clustering process is achieved on multidimensional feature vectors that represent the cosmetic defects. The performance of the GK algorithm may be considered similar to a human inspector which is between 85percent and 90percent approximately. However, the fuzzy clustering technique has the advantage to be very consistent, contrary to a human inspector that can change her/his mind due to subjective influences. The paper also presents the comparison between the fuzzy approach and the artificial neural network approach. The problem faced in this work also helped to compare the performance of FC algorithms with ANN in real world applications. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Havens:2008:fuzz,
author = "Timothy C. Havens and James M. Keller and Mihail Popescu and James C. Bezdek",
title = "Ontological Self-Organizing Maps for Cluster Visualization and Functional Summarization of Gene Products using Gene Ontology Similarity Measures",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0033.pdf},
url = {},
size = {},
abstract = {This paper presents an ontological self-organizing map (OSOM), which is used to produce visualization and functional summarization information about gene products using Gene Ontology (GO) similarity measures. The OSOM is an extension of the self-organizing map as initially developed by Kohonen, which trains on data composed of sets of terms. Term-based similarity measures are used as a distance metric as well as in the update of the OSOM training procedure. We present an OSOM-based visualization method that shows the cluster tendency of the gene products. Also demonstrated is an OSOM-based functional summarization which produces the most representative term(s) (MRT) from the GO for each OSOM prototype and, subsequently, each gene product cluster. We validated the results of our method by applying the OSOM to a well-studied set of gene products. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Bosc:2008:fuzz,
author = "Patrick Bosc and Olivier Pivert",
title = "On the Division Operator for Probabilistic and Possibilistic Relational Databases",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0037.pdf},
url = {},
size = {},
abstract = {This paper is situated in the area of imprecise (probabilistic and possibilistic) databases. Any imprecise database has a canonical interpretation as a set of more or less possible regular databases, also called worlds. In order to manipulate such databases in a safe and efficient way, a constrained framework has been previously proposed, where a restricted number of querying operations are permitted (selection, union, projection and foreign-key join which can handle attributes taking imprecise values). The key for efficiency resides in the fact that these operators do not require to make computations explicitly over all the more or less possible worlds. The division operation is dealt with in this paper and the impact of the uncertainty model on the processing technique is particularly studied. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li:2008:fuzz,
author = "Yongming Li ",
title = "Fuzzy Finite Automata and Fuzzy Monadic Second-Order Logic",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0040.pdf},
url = {},
size = {},
abstract = {We introduce fuzzy monadic second-order (LMSO-) logic and prove that the behaviours of fuzzy finite automata with membership values in an MV-algebra are precisely the fuzzy languages definable with sentences of our LMSO logic. This generalizes Büchi's and Elgot's fundamental theorems to fuzzy logic setting. We also consider fuzzy first-order logic and show that star-free fuzzy languages and aperiodic fuzzy languages introduced here coincide with the fuzzy first-order definable ones. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Honda:2008:fuzz,
author = " Katsuhiro Honda and Takahiro Ohyama and Hidetomo Ichihashi and Akira Notsu",
title = " FCM-Type Switching Regression with Alternating Least Squares Method",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0043.pdf},
url = {},
size = {},
abstract = {Fuzzy c-Regression Models (FCRM) performs switching regression based on a Fuzzy c-Means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal variables (categorical observations) are quantified so that they suit the current model, and has been applied to FCM-type fuzzy clustering in order to characterize each cluster considering mutual relation among categories. This paper proposes two new algorithms for handling mixed measurement situations in FCM-type switching regression based on the alternating least squares method. The iterative algorithms include additional optimal scaling steps for calculating numerical scores of categorical variables. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wu:2008:fuzz,
author = "Kewei Wu and Zhao Xie and Jun Gao and Wengang Feng",
title = "FCM in Novel Application of Science and Technology Progress Monitor System",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0044.pdf},
url = {},
size = {},
abstract = {This paper focuses on the issues about the complex relations in large-scale FCM, and then proposes a promising method for weight global optimization with local inference to analyze and predict indexes in Anhui sci-tech progress monitor system. Firstly, a new concept, unbalanced degree, is introduced for standard evaluation in FCM model to modify the weight assessment factors and result in the satisfied convergence rate. Secondly, relations between unbalanced degree and convergence error are also presented for further analysis with training error and guarantee on perfect condition in model. Thirdly, local inference in FCM is discussed to enhance prediction accuracy rate. Finally, experimental result reveals successful application of FCM in large-scale complex sci-tech systems. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Wan:2008:fuzz,
author = "Jia-Ren Wan and Ji-Chang Lo",
title = "LMI Relaxations for Nonlinear Fuzzy Control Systems Via Homogeneous Polynomials",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0045.pdf},
url = {},
size = {},
abstract = {Based on recent results on homogeneous polynomially parameter-dependent (HPPD) solutions to parameter-dependent LMIs (PD-LMIs) that arise from robust stability of linear parameter varying (LPV) systems, we investigate the relaxed conditions characterized by parameter-dependent LMIs (PD-LMIs) in terms of firing strength belonging to the unit simplex, exploiting the algebraic property of Pólya's Theorem to construct a family of finite-dimensional LMI relaxations. The main contribution of this paper is that sets of relaxed LMIs are parameterized in term of the polynomial degree d. As d increases, progressively less conservative LMI conditions are generated, being easier satisfied due to more freedom provided by new variables involved. An example to illustrate the relaxation is provided. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Qiu:2008:fuzz,
author = "Jianbin Qiu and Gang Feng and Jie Yang",
title = "Delay-Dependent Robust H∞ Filtering Design for Uncertain Discrete-Time T-S Fuzzy Systems with Interval Time-Varying Delay",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0046.pdf},
url = {},
size = {},
abstract = {This paper investigates the problem of delay-dependent robust H∞ filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler's Lemma, a new sufficient condition for robust H∞ performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hagras:2008:fuzz,
author = "Hani Hagras ",
title = " Developing a Type-2 FLC Through Embedded Type-1 FLCs",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0048.pdf},
url = {},
size = {},
abstract = {Type-1 Fuzzy Logic Controllers (FLCs) have been widely employed in many control applications as they give a good performance and it is relatively easy to extract the type-1 FLC parameters from experts. However, type-1 FLCs cannot fully handle the encountered uncertainties in changing unstructured environments as they use crisp type-1 fuzzy sets. Consequently, in order for type-1 FLCs to provide a satisfactory performance in face of high levels of uncertainties, some common practices are followed including continuously tuning the type-1 FLC or providing a set of type-1 FLCs where each FLC handles specific operation conditions. Alternatively, type-2 FLCs can handle uncertainties to give a better control performance. However, it is relatively challenging to extract from experts the Footprint of Uncertainty (FOU) information and consequently the type-2 fuzzy sets for type-2 FLCs. In this paper, we will present a novel method for generating the input and output type-2 fuzzy sets so that their FOUs can capture the faced uncertainties. The proposed method will generate a type-2 FLC that will try to embed the type-1 FLCs corresponding to the various operation conditions faced so far besides embedding a large number of other embedded type-1 FLCs. This will allow the type-2 FLC to handle the uncertainties trough a big number of embedded type-1 FLCs to produce a smooth and robust control performance. We will show through real world experiments how the developed type-2 FLC will handle the uncertainties and give a smooth control response that outperforms the individual and aggregated type-1 FLCs. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hagras2:2008:fuzz,
author = "Hani Hagras and Ian Packham and Yann Vanderstockt and Nicholas McNulty and Abhay Vadher and Faiyaz Doctor",
title = " An Intelligent Agent Based Approach for Energy Management in Commercial Buildings",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0049.pdf},
url = {},
size = {},
abstract = {Global warming is becoming one of the serious issues facing humanity. Several initiatives have been introduced to deal with global warming including the Kyoto Protocol which assigned mandatory targets for the reduction of greenhouse gas emissions to signatory nations. However, over the last decade, commercial buildings worldwide have experienced massive growth in energy costs. This was caused by the expansion in the use of air conditioning and artificial lighting as well as an ever increasing energy demand for computing services. Existing Building Management Systems (BMSs) have, generally, failed to fully optimize energy consumption in commercial buildings. This is because they lack control systems that can react intelligently and automatically to anticipated changes in ambient weather conditions and the many other environmental variables typically associated with large buildings.In this paper, we present a novel agent based system entitled Intelligent Control of Energy (ICE) for energy management in commercial buildings. ICE uses different Computational Intelligence (CI) techniques (including fuzzy systems, neural networks and genetic algorithms) to 'learn' a buildings thermal response to many variables including the outside weather conditions, internal occupancy requirements and building plant responses. ICE then uses CI based algorithms which work in real-time with the building's existing BMS to minimize the building's energy demand. We will show how the use of ICE will allow significant energy cost savings, while still maintaining customer-defined comfort levels. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Li2:2008:fuzz,
author = "Qiaoxing Li and Jianmei Yang ",
title = "Aggregation of Fuzzy Opinions with an Area Between the Centroid Point and the Original Point Under Group Decision Making",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0050.pdf},
url = {},
size = {},
abstract = {It is an important issue to obtain the consensus opinion under group decision making. In this paper, we propose a new approach to aggregate the experts' opinions which is based on the area between the centroid points of fuzzy numbers and the original point. The opinions of experts are represented by positive fuzzy numbers which include the normal and the non-normal trapezoidal fuzzy numbers as well as interval numbers. A new index that the consensus of each expert to others is constructed by using a similarity measure under the area. We also take into consideration the importance of each expert in the process of aggregation. The operational procedure is simple and the numerical examples show its reliability. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Hwang:2008:fuzz,
author = "Chih-Lyang Hwang and Ching-Chang Wong",
title = "Fuzzy Mixed H2/H∞ Optimized Design of Decentralized Control for Nonlinear Interconnected Dynamic Delay Systems",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0053.pdf},
url = {},
size = {},
abstract = {Each subsystem of a nonlinear interconnected dynamic delay system (NIDDS) is first approximated by a weighted combination of L transfer function delay systems (TFDSs). The H2-norm of the difference between the transfer function of the reference model and the closed-loop transfer function of the kth TFDS of subsystem i is then minimized to obtain a suitable frequency response. Because the output disturbance of the kth TFDS, including the interconnections coming from the other subsystems, the approximation error of the ith subsystem, and the interactions resulting from the other TFDSs, is not small and includes various frequencies, the H∞-norm of the weighted sensitivity function between the output disturbance and its corresponding output of the kth TFDS is simultaneously minimized to attenuate its effect. In addition, an appropriate selection of the weighted function for the sensitivity can reject the specific mode of the output disturbance. Finally, the stability of the overall system is verified by the concept of Ln2-stable with finite gain. },
keywords = {Decentralized control, Fuzzy linear model, Nonlinear interconnected dynamic delay system, H2 -optimization, H∞, },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu2:2008:fuzz,
author = "Jian-Xin Xu and Chao Xue and Chang-Chieh Hang and Krishna V. Palem",
title = "A Fuzzy Control Chip Based on Probabilistic CMOS Technology",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0055.pdf},
url = {},
size = {},
abstract = {In this work, a novel approach using Probabilistic CMOS (PCOMS) technology is used to reduce the energy consumption of a fuzzy PID (proportional-integral-derivative) controller. Energy saving is achieved through designing a probabilistic circuit which deliberately reduces the supply voltage of some less significant bits. The fuzzy PID controller consists of 15 bits with floating point representation. Through numerical simulations and VHDL validation, the fuzzy PID can obtain a satisfactory tradeoff with about 4percent deviation while achieving a total energy saving of about 32percent. Through error analysis, a fuzzy PID is redesigned to tolerate more of randomness in the control signals, hence obtain a better steady state performance while achieving an energy saving of about 51percent. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Yu:2008:fuzz,
author = "Yongguang Yu and Han-Xiong Li",
title = "Stable Flocking of Mobile Formation in 3-Dimensional Space",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0056.pdf},
url = {},
size = {},
abstract = {The paper investigates the flocking behaviors of multi-agent formation in 3-dimensional space which are based on leader following. A class of decentralized control laws for a group of mobile agents are proposed under the conditions that the topology of the control interconnections is fixed and dynamically time-variant, respectively. These control laws are a combination of attractive/repulsive and alignments forces which can guarantee the collision avoidance and cohesion of the formation and an aggregate motion along the same heading direction of the leader. According to the algebraic graph theory, differential inclusions and non-smooth analysis, we model the interconnection relationship of multi-agent formation, and achieve the stability analysis of the system by Lyapunov theory. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Chiang:2008:fuzz,
author = "Chiang-Cheng Chiang and Shih-Wei Wang ",
title = "Observer-Based Robust Adaptive Fuzzy Control of Uncertain Nonlinear Systems with Delayed Output",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0058.pdf},
url = {},
size = {},
abstract = {In this paper, an observer-based robust adaptive fuzzy controller is proposed to deal with the output tracking control problem for a class of uncertain single-input single-output (SISO) nonlinear systems with output delay and unmatched uncertainties. Within this scheme, the state observer is applied for estimating all states which are not available for measurement in the system, and then fuzzy logic systems and some adaptive laws are used to approximate the unknown nonlinear functions and the unknown upper bounds of unmatched uncertainties. By constructing an appropriate Lyapunov function and solving Lyapunov equations, the proposed robust adaptive fuzzy controller can guarantee that the asymptotic stabilization and the output tracking performance of the whole closed-loop system can be achieved. Finally, an example is given to illustrate the effectiveness of the proposed approach. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ludwig:2008:fuzz,
author = "Simone A. Ludwig ",
title = "Fuzzy Match Score of Semantic Service Match",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0060.pdf},
url = {},
size = {},
abstract = {Automatic discovery of services is a crucial task for the e-Science and e-Business communities. Finding a suitable way to address this issue has become one of the key points to convert the Web into a distributed source of computation, as it enables the location of distributed services to perform a required functionality. To provide such an automatic location, the discovery process should be based on the semantic match between a declarative description of the service being sought and a description being offered. This problem requires not only an algorithm to match these descriptions, but also a language to declaratively express the capabilities of services. The proposed matchmaking approach is based on semantic descriptions for service attributes, descriptions and metadata. For the ranking of service matches a match score is calculated whereby the weight values are either given by the user or estimated using a fuzzy approach. An evaluation of both weight assignment approaches is conducted identifying in which scenario one works better than the other. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ichihashi:2008:fuzz,
author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Eri Miyamoto ",
title = "FCM Classifier for High-Dimensional Data",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0061.pdf},
url = {},
size = {},
abstract = {A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional data. This paper proposes a way of directly handling high-dimensional data in the FCM clustering and classification. The proposed classifier without any preprocessing outperforms the k-nearest neighbor (k-NN) classifier with PCA on the benchmark set of COREL image collection. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ichihashi2:2008:fuzz,
author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Keichi Ohta ",
title = "Fuzzy c-Means Classifier with Particle Swarm Optimization",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0062.pdf},
url = {},
size = {},
abstract = {Fuzzy c-means-based classifier derived from a generalized fuzzy c-means (FCM) partition and optimized by particle swarm optimization (PSO) is proposed. The procedure consists of two phases. The first phase is an unsupervised clustering, which is not initialized with random numbers, hence being deterministic. The second phase is a supervised classification. The parameters of membership functions and the location of cluster centers are optimized by the PSO and cross validation (CV) procedures.Since different types of classifiers work best for different types of data, our strategy is to parameterize the classifier and tailor it to individual data set. The FCM classifier outperforms well established methods such as k-nearest neighbor classifier (k-NN), support vector machine (SVM) and Gaussian mixture classifier (GMC) in terms of 10-fold CV and three-way data splits. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Ichihashi3:2008:fuzz,
author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Takao Hattori ",
title = "Classifier of BOLD Signals from Active and Inactive Brain States Using FCM Clustering and Evolutionary Algorithms",
booktitle = "2008 IEEE World Congress on Computational Intelligence",
year = 2008,
editor = "Jun Wang",
pages = {--},
address = "Hong Kong",
month = "1-6 June",
organization ="IEEE Computational Intelligence Society",
publisher = "IEEE Press",
note = {},
ISBN13 = "978-1-4244-1819-0",
file = {FS0063.pdf},
url = {},
size = {},
abstract = {A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. This paper reports a result of test on a data set with high-dimensional feature values. For classifying the blood oxygen level dependent (BOLD) responses of the brain, a way of directly handling high-dimensional fMRI signals is applied. Our goal is to distinguish the BOLD responses to recalling tasks from those to resting (i.e., a binary classification problem). We use the signals from wide areas of the brain, which forms a set of high dimensional data vectors. The FCM classifier is compared with support vector machine (SVM). SVM is a high performance classifier and well suited for binary classification problems, since the size of the kernel matrix of SVM depends only on the number of instances. The error rate on the test set by the FCM classifier surpassed the SVM, though the SVM can easily handle sets of high dimensional feature vectors. },
notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)
@inproceedings(Xu3:2008:fuzz,
author = "Sheng Xu and Huifang Zhao and Xuanli Lv",
title = "A Grey SVM Based Model for Patent Application Filings Forecasting",
booktitle